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Arboriculture is the science and practice of tree care and management. Urban forestry is about greening our towns and cities to create a healthy and sustainable urban environment. Read more
Arboriculture is the science and practice of tree care and management. Urban forestry is about greening our towns and cities to create a healthy and sustainable urban environment. Together, these two closely related disciplines have a vital role to play in creating a liveable environment. The numerous environmental, economic and social benefits of urban trees and woodlands can dramatically improve the quality of life in our towns and cities and this has been identified as a government priority in several recent policy documents.

This on-line MSc Arboriculture and Urban Forestry, awarded from the University of Central Lancashire, is a ground-breaking course which recognises the multidiscipline approach of the subjects. The course aims to extend student's existing expertise to the full range of skills and knowledge of social, technical and strategic tree management issues now required by senior positions in the industry.
The MSc will encourage debate and critical evaluation of current practices and research within this field. The course will enable students to reflect on current issues and develop problem solving skills which encourage originality of thought on current issues within Arboriculture and Urban Forestry.

Year 1

Urban Development and Urban Greening

This module will examine the nature of the urban environment and the historical development of urban greenspace management. It will explore the current nature and extent of urban green space management in Britain and overseas and reflect on the role of urban trees and woodland in improving the quality of life for urban dwellers.

The Science of Tree Production and Establishment

This module will look at the latest techniques in establishing trees in urban areas and challenge conventional views on tree production, planting, landscaping and post-planting maintenance in the light of scientific advances in these areas.

Trees and Urban Planning

This module will explore key statutory and common laws concerned with the regulation and preservation of trees. It will consider trees in relation to the regulation of land used in terms of development control and reflect on the wider context of trees and planning in the development of urban landscapes.

Year 2

Tree Physiology and the Urban Environment

This module aims to advance the knowledge of students in arboricultural science and its applications that rely upon knowledge of a tree’s biological system and physiological functions. The module will investigate key areas of the physiology of trees, including modifications in tree physiology that satisfy different environmental conditions of the urban environment, tree defence systems and associated tree health care treatments. The learning outcomes of this module reflect the need for important findings from scientific investigations into tree physiology to be applied to tree management. This cross-fertilisation of tree science and tree management is much needed, and students will benefit from a deeper understanding of trees as living organisms and the influence of different management choices.

Research Methodology and Design

This module provides students with the essential personal, organisational, management, theoretical and statistical skills needed to work at Postgraduate Level. It will explore research philosophies, research process and design and the process of questionnaire development and design. The module will develop skills in advanced data organisation, presentation, dissemination and problem solving.

Tree Risk Management

This module will investigate the complex relationships between tree biomechanics, the development of defects and infection strategies for fungal diseases and other pathogens. The module will evaluate these facets in the wider context of tree risk management and the development of risk management strategies for tree populations.

Year 3

Masters Dissertation

The dissertation is a triple module and allows students to design and conduct a substantial piece of independent, supervised research in the field of arboriculture or urban forestry. The dissertation is an independent piece of academic work which allows the student to identify and work in an area of interest to them and manage the research process to agreed deadlines.

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In this one-year MSc programme, you have the opportunity to learn about how trees, people and agriculture can be combined in sustainably managed farms, forests and landscapes. Read more
In this one-year MSc programme, you have the opportunity to learn about how trees, people and agriculture can be combined in sustainably managed farms, forests and landscapes. There is a long tradition of agroforestry practice in many parts of the world, but recently it has become a major focus in international development and is now at the forefront of innovation in natural resource management. Bangor is a world leader in agroforestry with a fantastic reputation for its research activities and our graduates are either already employed when they start the course and/or have a strong track record in finding employment within the sector. Students and academic staff are active collaborators with international organisations such as the Tropical Agricultural Research and Higher Education Center, Costa Rica (CATIE) and the World Agroforestry Centre (ICRAF). You can expect to develop the skills required for a research and professional career from the comprehensive programme we offer.

The overall aim of the programme is to provide an integrated education in natural resource management, combining ecological and social dimensions of agricultural and forest sciences, focussed on application to real world systems where trees interact with agriculture. The programme is designed to develop both subject-specific knowledge and cognitive and key skills. The course has a world focus and the University has strong links with agroforestry organisations which means that many of our students have undertaken fieldwork in Africa, Asia, Europe and the Americas, as well as in Wales/UK. Besides fantastic overseas opportunities, we also have a university farm (Henfaes Research Centre) located a short distance outside of Bangor where many students carry out experiments for their final projects.

This course is accredited by the Institute of Chartered Foresters and gives partial fulfilment of Professional Membership Entry.

We work in partnership with the World Agroforestry Centre.


Course Structure
The programme has two parts. Part Part 1: runs from September to May and consists of five taught modules and a study tour. The taught part of the course is based on lectures, seminars, practical sessions and directed study, allowing an opportunity to examine a broad range of topics in detail and develop personal skills and expertise. A range of different assessment methods are used including reports, presentations, practical write-ups and online and written exams. Part 1 must be completed successfully before proceeding to Part 2, the dissertation phase.

Part 2: June to September is set aside for production of a dissertation on a topic selected by the student in consultation with their academic supervisor. Dissertations can be in almost any aspect of agroforestry that interests you; they can have a temperate or tropical focus, and can include field work either locally in Wales, elsewhere in the UK, or overseas.

Part 1 Subjects:

Agroforestry Systems and Practice: This module explores agroforestry systems and practices worldwide and introduces the concepts behind them. Through a series of case studies, the module explores ecological and biophysical interactions in agroforestry systems, and considers the range of social, economic and ecosystem benefits they deliver, including ways in which we are trying to reduce the environmental impact of food production and overcome constraints to food security.

Silviculture: The purpose of the module is to develop students’ understanding of the silviculture of single trees and trees in complex systems. This module develops an understanding of the principles and practice of silviculture, the place of silviculture in the sustainable cultivation of trees, and the role it plays in delivering ecosystem services from trees, woodlands and forests. We explore the unique characteristics of forest soils and of soil physical, chemical and biological properties, how these influence site productivity and how these are influenced by land management.

Natural Resource Management: The purpose of this module is to give students a theoretical understanding of the systems approach to managing natural resources to provide various ecosystem services, as well as a practical grounding in the ways in which natural resource managers can draw on a variety of knowledge sources to inform themselves and others of the impacts of land management interventions.

Research Planning and Communication: This module seeks to develop students’ understanding of the role of science and the scientific process in formulating and addressing context relevant questions, and communicating scientific output to different audiences. During the course of the module, students will devise, conduct and write up a policy-relevant scientific study.

Natural Resource Development: The purpose of this module is to introduce the international development context to students and to give a practical grounding in project planning. During the module, students will develop a full project proposal in line with funding guidelines for an agroforestry based natural resource development intervention.

Study Tour: We round off the taught part of the course with a study tour which gives students the opportunity to see the practical application of natural resource management principles that are discussed in earlier parts of the programme. During visits to areas which are managed for a range of objectives, you will meet and discuss with different stakeholders and collect information relevant to a specific research topic.

Part 2:

Dissertation: Execution and written presentation of a suitable scientific project which is devised by the student and an individual academic supervisor and validated by the Programme Director. A suitable project entails a worthwhile scientific question, of direct relevance to the degree programme being undertaken, established within the context of current knowledge and concepts that allows the formulation and testing of one or more hypotheses. This normally involves up to 5 months full-time work, typically including: 2-3 months for data collection from the field, laboratory or computer; 1-2 months for data analysis; and 1-2 months for writing-up.

Previous MSc dissertation projects and training courses held in collaboration with the World Agroforestry Centre can be viewed here.

Professional Accreditation

This degree is accredited by the Institute of Chartered Foresters (ICF) and qualifies students for associate membership.

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This unique transdisciplinary course, open to people from all backgrounds, offers a special focus on contemporary social sculpture, ecological citizenship, connective eco-social practices, cultural activism, expanded art practices and transformative, creative action. Read more
This unique transdisciplinary course, open to people from all backgrounds, offers a special focus on contemporary social sculpture, ecological citizenship, connective eco-social practices, cultural activism, expanded art practices and transformative, creative action. It enables you to explore strategies of engagement, agency and the relationship between imagination and transformation. The programme also makes special reference to the proposals and legacies of Joseph Beuys, Schiller and Goethe, as well as other pedagogies of transformation such as Joanna Macy's and Paulo Freire's. It introduces theoretical and philosophical frameworks, with a special emphasis on phenomenology and experiential knowing; explores the relationship of social sculpture to ecological sustainability and offers practice-based research methodologies and creative strategies as the basis for developing individual and collaborative social sculpture processes, interdisciplinary expanded arts and reflective social practice.

The MA is Social Sculpture is, with the MA in Sound Arts, one of two taught postgraduate courses for socially-engaged artists, composers and transdisciplinary practitioners currently offered by the School of Arts at Oxford Brookes University. These MAs share two core modules in Creative Strategies and Phenomenological Methods of practice-based work. These shared modules enable cross-pollination and potential for collaboration between social sculpture and connective practice practitioners and those working in the field of sound arts. The MA in Social Sculpture is linked to the Social Sculpture Research Unit and is part of a thriving post-graduate research culture. There are opportunities to volunteer in social sculpture projects like University of the Trees: Lab for an Eco-Social Future.

Why choose this course?

The MA in Social Sculpture is an internationally renowned programme, running since 2006, linked to the Social Sculpture Research Unit at Oxford Brookes. A dedicated team of international specialists and emerging practitioners delivers innovative cross-disciplinary and socially-engaged creative practices that many students have described as 'life changing'.

-Participating in a community of dialogue and reflection: the unique 'Feedback Forum' approach which runs throughout the programme replaces the traditional art-school 'crit', offering a radical, supportive and creative form of feedback on your work. Another special feature is the regular MA Forum, in which students and staff meet to discuss creative practice in a supportive and stimulating environment. It also offers fortnightly individual tutorials and small group supervision.
-Coherent and unique teaching approach: a carefully sequenced set of modules enable you to uncover, explore and develop your own concerns within the field of contemporary social sculpture, creative cultural action and other interdisciplinary connective practices.
-Research culture and opportunities beyond the programme: MA Social Sculpture students are welcome to participate in 7 day-long 'PhD Social Sculpture Fora' per year. This is part of a stimulating environment where tutors, alumni, research fellows and student interns work closely together in the Social Sculpture Research Unit, and in projects like University of the Trees: Lab for New Knowledge and an Eco-Social Future.
-Based in the School of Arts' beautiful Richard Hamilton Building: situated very close to the city centre in a wooded landscape and arboretum, it offers excellent technical support; well-equipped workshops in video, photography, sound, artists books, printmaking and a variety of 3-D processes; a well- equipped library with materials appropriate to our programme and dedicated support for practice-based research students. There is bookable installation space, a group studio base and 24/7 studio access.
-Wider context: research and teaching programmes in the School of Arts are linked to some of Oxford’s leading cultural organisations such as Modern Art Oxford, and the annual Social Sculpture Festival of MA student work takes place in an around Oxford, using accessible local venues as a hub. You are encouraged to make links with local communities and social and ecological organisations as well as being able to design certain projects related to their home contexts. Once you graduate from the programme you have the opportunity to participate in the annual Social Sculpture Platform which is open to the public.

This course in detail

MA in Social Sculpture students take five compulsory modules - Creative Strategies 1 and 2, Social Sculpture 1 and 2 and a Major Project - in which they develop their particular concerns.

PGDip in Social Sculpture students take four compulsory modules - Creative Strategies 1 and 2 and Social Sculpture 1 and 2.

Teaching and learning

Our teaching methods include:
-Seminars and lectures on interdisciplinary creative practice, practice-based research, phenomenological root methodologies and social sculpture.
-Team teaching in group seminars, involving research methodologies for practice-based research.
-Feedback from staff and students during group feedback sessions, in which you receive constructive feedback on your work.
-Staff-led group discussions arising out of practical presentations.
-Regular individual tutorials that address your research concerns.
-Introductions to creative strategies for generating and making practice-based social sculpture and other forms of connective cultural action and reflective social practice.
-Introductions to the School of Arts technical facilities.
-Induction sessions with subject librarians.

The learning methods include:
-Regular forums where staff and students formulate and articulate responses to work.
-Social sculpture and interdisciplinary creative practice presentations.
-Presentations of practical research.
-The researching and writing of reflective reports, assignments and self-evaluations.
-Private research and study.
-Presentations to peers and group feedback via the 'feedback forum' approach to 'reception theory' in practice.

Careers and professional development

In this unique programme graduates develop excellent creative capacities and new ways of thinking that enable them to identify and develop interdisciplinary arenas and contexts for public engagement with specific communities, organisations and other constituencies.

A strong aspect of the programme is the way it enables graduates to return to existing professions and contexts in new ways: as interdisciplinary practitioners with insightful understandings, greatly enhanced imaginal capacities and knowledge of new forms of reflective and interdisciplinary connective practice.

Many Social Sculpture graduates continue as social sculpture practitioners or eco-cultural activists, whilst others develop careers related to their knowledge, expertise or interests, for example within organisational change, social enterprise programmes, festival management, tertiary education, agro-ecology, arts administration; arts and music teaching, medical humanities, educators and practitioners in arts for health, promoting ecological citizenship, community cross artform work and as sustainability activists.

These diverse career possibilities have much to do with the close relationship between the content and the pedagogic approaches offered on the MA Social Sculpture programme with its focus on experiential knowing, active citizenship and connective practices.

Combining the rigour of a traditional academic programme with innovative practical and vocational components makes graduates well placed for roles as practitioners as well as for further research in territory that includes the arts and sustainability, ecological citizenship, individual and community change processes, cultural and ecological activism and the field of contemporary social sculpture and connective aesthetics.

The methodologies taught also enable new forms of interdisciplinary and postdisciplinary practice and research.

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Programme structure. The programme offers four "core" modules, taken by all students, along with a variety of elective modules from which students can pick and choose. Read more
Programme structure
The programme offers four "core" modules, taken by all students, along with a variety of elective modules from which students can pick and choose. There are examinations and coursework in eight modules altogether, including the four core modules. Additionally, all students complete a dissertation.

Core modules
0.Probability and stochastics. This course provides the basics of the probabilistic ideas and mathematical language needed to fully appreciate the modern mathematical theory of finance and its applications. Topics include: measurable spaces, sigma-algebras, filtrations, probability spaces, martingales, continuous-time stochastic processes, Poisson processes, Brownian motion, stochastic integration, Ito calculus, log-normal processes, stochastic differential equations, the Ornstein-Uhlenbeck process.


0.Financial markets. This course is designed to cover basic ideas about financial markets, including market terminology and conventions. Topics include: theory of interest, present value, future value, fixed-income securities, term structure of interest rates, elements of probability theory, mean-variance portfolio theory, the Markowitz model, capital asset pricing model (CAPM), portfolio performance, risk and utility, portfolio choice theorem, risk-neutral pricing, derivatives pricing theory, Cox-Ross-Rubinstein formula for option pricing.


0.Option pricing theory. The key ideas leading to the valuation of options and other important derivatives will be introduced. Topics include: risk-free asset, risky assets, single-period binomial model, option pricing on binomial trees, dynamical equations for price processes in continuous time, Radon-Nikodym process, equivalent martingale measures, Girsanov's theorem, change of measure, martingale representation theorem, self-financing strategy, market completeness, hedge portfolios, replication strategy, option pricing, Black-Scholes formula.


0.Financial computing I. The idea of this course is to enable students to learn how the theory of pricing and hedging can be implemented numerically. Topics include: (i) The Unix/Linux environment, C/C++ programming: types, decisions, loops, functions, arrays, pointers, strings, files, dynamic memory, preprocessor; (ii) data structures: lists and trees; (iii) introduction to parallel (multi-core, shared memory) computing: open MP constructs; applications to matrix arithmetic, finite difference methods, Monte Carlo option pricing.


0.Interest rate theory. An in-depth analysis of interest-rate modelling and derivative pricing will be presented. Topics include: interest rate markets, discount bonds, the short rate, forward rates, swap rates, yields, the Vasicek model, the Hull-White model, the Heath-Jarrow-Merton formalism, the market model, bond option pricing in the Vasicek model, the positive interest framework, option and swaption pricing in the Flesaker-Hughston model.

Elective modules

0.Portfolio theory. The general theory of financial portfolio based on utility theory will be introduced in this module. Topics include: utility functions, risk aversion, the St Petersburg paradox, convex dual functions, dynamic asset pricing, expectation, forecast and valuation, portfolio optimisation under budget constraints, wealth consumption, growth versus income.


0.Information in finance with application to credit risk management. An innovative and intuitive approach to asset pricing, based on the modelling of the flow of information in financial markets, will be introduced in this module. Topics include: information-based asset pricing – a new paradigm for financial risk management; modelling frameworks for cash flows and market information; applications to credit risk modelling, defaultable discount bond dynamics, the pricing and hedging of credit-risky derivatives such as credit default swaps (CDS), asset dependencies and correlation modelling, and the origin of stochastic volatility.

0.Mathematical theory of dynamic asset pricing. Financial modelling and risk management involve not only the valuation and hedging of various assets and their positions, but also the problem of asset allocation. The traditional approach of risk-neutral valuation treats the problem of valuation and hedging, but is limited when it comes to understanding asset returns and the behaviour of asset prices in the real-world 'physical' probability measure. The pricing kernel approach, however, treats these different aspects of financial modelling in a unified and coherent manner. This module introduces in detail the techniques of pricing kernel methodologies, and its applications to interest-rete modelling, foreign exchange market, and inflation-linked products. Another application concerns the modelling of financial markets where prices admit jumps. In this case, the relation between risk, risk aversion, and return is obscured in traditional approaches, but is made clear in the pricing kernel method. The module also covers the introduction to the theory of Lévy processes for jumps and its applications to dynamic asset pricing in the modern setting.

0.Financial computing II: High performance computing. In this parallel-computing module students will learn how to harness the power of a multi-core computer and Open MP to speed up a task by running it in parallel. Topics include: shared and distributed memory concepts; Message Passing and introduction to MPI constructs; communications models, applications and pitfalls; open MP within MPI; introduction to Graphics Processors; GPU computing and the CUDA programming model; CUDA within MPI; applications to matrix arithmetic, finite difference methods, Monte Carlo option pricing.


0.Risk measures, preference and portfolio choice. The idea of this module is to enable students to learn a variety of statistical techniques that will be useful in various practical applications in investment banks and hedge funds. Topics include: probability and statistical models, models for return distributions, financial time series, stationary processes, estimation of AR processes, portfolio regression, least square estimation, value-at-risk, coherent risk measures, GARCH models, non-parametric regression and splines.

Research project

Towards the end of the Spring Term, students will choose a topic to work on, which will lead to the preparation of an MSc dissertation. This can be thought of as a mini research project. The project supervisor will usually be a member of the financial mathematics group. In some cases the project may be overseen by an external supervisor based at a financial institution or another academic institution.

Read less
Programme structure. The programme offers five "core" modules, taken by all candidates, along with a variety of elective modules from which students can pick and choose. Read more
Programme structure

The programme offers five "core" modules, taken by all candidates, along with a variety of elective modules from which students can pick and choose. There are lectures, examinations and coursework in eight modules altogether, including the five core modules. Additionally, all students complete an individual research project on a selected topic in financial mathematics, leading to the submission of a dissertation.

Core modules

Probability and stochastics. This course provides the basics of the probabilistic ideas and mathematical language needed to fully appreciate the modern mathematical theory of finance and its applications. Topics include: measurable spaces, sigma-algebras, filtrations, probability spaces, martingales, continuous-time stochastic processes, Poisson processes, Brownian motion, stochastic integration, Ito calculus, log-normal processes, stochastic differential equations, the Ornstein-Uhlenbeck process.

Financial markets. This course is designed to cover basic ideas about financial markets, including market terminology and conventions. Topics include: theory of interest, present value, future value, fixed-income securities, term structure of interest rates, elements of probability theory, mean-variance portfolio theory, the Markowitz model, capital asset pricing model (CAPM), portfolio performance, risk and utility, portfolio choice theorem, risk-neutral pricing, derivatives pricing theory, Cox-Ross-Rubinstein formula for option pricing.

Option pricing theory. The key ideas leading to the valuation of options and other important derivatives will be introduced. Topics include: risk-free asset, risky assets, single-period binomial model, option pricing on binomial trees, dynamical equations for price processes in continuous time, Radon-Nikodym process, equivalent martingale measures, Girsanov's theorem, change of measure, martingale representation theorem, self-financing strategy, market completeness, hedge portfolios, replication strategy, option pricing, Black-Scholes formula.


Interest rate theory. An in-depth analysis of interest-rate modelling and derivative pricing will be presented. Topics include: interest rate markets, discount bonds, the short rate, forward rates, swap rates, yields, the Vasicek model, the Hull-White model, the Heath-Jarrow-Merton formalism, the market model, bond option pricing in the Vasicek model, the positive interest framework, option and swaption pricing in the Flesaker-Hughston model.

Financial computing I. The idea of this course is to enable students to learn how the theory of pricing and hedging can be implemented numerically. Topics include: (i) The Unix/Linux environment, C/C++ programming: types, decisions, loops, functions, arrays, pointers, strings, files, dynamic memory, preprocessor; (ii) data structures: lists and trees; (iii) introduction to parallel (multi-core, shared memory) computing: open MP constructs; applications to matrix arithmetic, finite difference methods, Monte Carlo option pricing.

Elective modules

Portfolio theory. The general theory of financial portfolio based on utility theory will be introduced in this module. Topics include: utility functions, risk aversion, the St Petersburg paradox, convex dual functions, dynamic asset pricing, expectation, forecast and valuation, portfolio optimisation under budget constraints, wealth consumption, growth versus income.

Information in finance with application to credit risk management. An innovative and intuitive approach to asset pricing, based on the modelling of the flow of information in financial markets, will be introduced in this module. Topics include: information-based asset pricing – a new paradigm for financial risk management; modelling frameworks for cash flows and market information; applications to credit risk modelling, defaultable discount bond dynamics, the pricing and hedging of credit-risky derivatives such as credit default swaps (CDS), asset dependencies and correlation modelling, and the origin of stochastic volatility.


Mathematical theory of dynamic asset pricing. Financial modelling and risk management involve not only the valuation and hedging of various assets and their positions, but also the problem of asset allocation. The traditional approach of risk-neutral valuation treats the problem of valuation and hedging, but is limited when it comes to understanding asset returns and the behaviour of asset prices in the real-world 'physical' probability measure. The pricing kernel approach, however, treats these different aspects of financial modelling in a unified and coherent manner. This module introduces in detail the techniques of pricing kernel methodologies, and its applications to interest-rete modelling, foreign exchange market, and inflation-linked products. Another application concerns the modelling of financial markets where prices admit jumps. In this case, the relation between risk, risk aversion, and return is obscured in traditional approaches, but is made clear in the pricing kernel method. The module also covers the introduction to the theory of Lévy processes for jumps and its applications to dynamic asset pricing in the modern setting.


Financial computing II: High performance computing. In this parallel-computing module students will learn how to harness the power of a multi-core computer and Open MP to speed up a task by running it in parallel. Topics include: shared and distributed memory concepts; Message Passing and introduction to MPI constructs; communications models, applications and pitfalls; open MP within MPI; introduction to Graphics Processors; GPU computing and the CUDA programming model; CUDA within MPI; applications to matrix arithmetic, finite difference methods, Monte Carlo option pricing.

Risk measures, preference and portfolio choice. The idea of this module is to enable students to learn a variety of statistical techniques that will be useful in various practical applications in investment banks and hedge funds. Topics include: probability and statistical models, models for return distributions, financial time series, stationary processes, estimation of AR processes, portfolio regression, least square estimation, value-at-risk, coherent risk measures, GARCH models, non-parametric regression and splines.

Research project

Towards the end of the Spring Term, students will choose a topic for an individual research project, which will lead to the preparation and submission of an MSc dissertation. The project supervisor will usually be a member of the Brunel financial mathematics group. In some cases the project may be overseen by an external supervisor based at a financial institution or another academic institution.

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With the global human population set to rise to over nine billion by 2050, and our trees facing new plant threats, we urgently need sustainable solutions that will allow us to increase the global food supply while preserving the integrity of agricultural and non-agricultural ecosystems. Read more

Programme description

With the global human population set to rise to over nine billion by 2050, and our trees facing new plant threats, we urgently need sustainable solutions that will allow us to increase the global food supply while preserving the integrity of agricultural and non-agricultural ecosystems.

Food production has tripled in the last forty years, but one billion people still go hungry every year. On average 30% of all food produced is wasted in the pathway from 'field to fork'. With the global human population set to rise from seven to over nine billion by 2050, we urgently need sustainable solutions that will allow us to increase the global food supply while preserving the integrity of agricultural and non-agricultural ecosystems.

Our trees and forests face new plant health threats which in turn threaten areas of great natural beauty and diversity, and affect both rural and urban landscapes. Our unique MSc Sustainable Plant Health will give you the opportunity to develop your understanding of the vital role of plant health, applying your skills by conducting laboratory and field studies.

This programme is primarily aimed at graduates wishing to pursue a career in plant protection in agriculture, horticulture, forestry or urban settings, and also careers in policy development and implementation, plant health inspection, academic and industrial research, consultancy and conservation management, and private industry.
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Programme structure

The full time 12 month programme involves two semesters of classes followed by an individual research project. The curriculum consists of two core and four elective taught courses, followed by a period of individual dissertation project work. Field trips will also form a crucial part of this course.

Core courses typically include*:
•Forensic Plant Health: lectures, lab classes and field visits providing a foundation in plant health investigation
•Plant Health in a Global Context: lectures and visits providing a global context for exploring plant health issues
•Dissertation
•Students with little background in plant health biology will take the elective course on the Fundamentals of Plant Health

Option courses allow students to tailor the degree programme according to their interests. Option courses may include*:
•Applications in Ecological Economics
•Atmospheric Quality and Global Change
•Case Studies in Sustainable Development
•Climate Change and Corporate Strategy
•Ecosystem Services 1: Ecosystem Dynamics and Functions
•Ecosystem Services 2: Ecosystem Values and Management
•Environmental Impact Assessment
•Foundations in Ecological Economics
•Frameworks to Assess Food Security
•Human Dimensions of Environmental Change and Sustainability
•Integrated Resource Management
•Integrated Resource Planning
•Interrelationships in Food Systems
•Land Use/Environmental Interactions
•Principles of Environmental Sustainability
•Soil Protection and Management
•Soil Science Concepts and Application
•Sustainability of Food Production
•Understanding Environment and Development

*Please note: courses are offered subject to timetabling and availability and are subject to change each year.

Learning outcomes

On completion of this course our graduates will have gained:
•Specialist knowledge and understanding of plant health, and its evaluation, impact and management
•Skills to detect and identify agents detrimental to plant health
•An understanding of the nature and diversity of plant health interactions
•The ability to develop strategies for plant health management taking into account their impact on agricultural and non-agricultural ecosystems
•Knowledge of the relevance of plant health to sustainability and food security
•Improved analytical skills and critical thinking

Career opportunities

Plant health scientists are employed in a range of vocations: environmental consultancy, research, overseas development, agriculture, horticulture, forestry, urban planning, policy development, plant inspection and management. Long term career prospects are strong as agricultural scientists will continue to be needed to balance increased output with protection and preservation of ecosystems.

Our graduates will gain particularly valuable skills due to our programme's unique approach looking at impacts across ecosystems. They also benefit from the applied nature of the course allowing them to use their practical skills in a range of field trip environments with expert supervision.

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This degree, offered by the Department of Computer Science, will teach you both the foundational aspects and the practical skills that prepare you for handling… Read more
This degree, offered by the Department of Computer Science, will teach you both the foundational aspects and the practical skills that prepare you for handling and analysing different types of data in different fields, thus responding to the needs of a huge variety of companies and organisations, from retailers such as Tesco or Amazon, to manufacturers like BMW, to health-care providers, and to public administration.

See the website https://www.royalholloway.ac.uk/computerscience/coursefinder/mscdatascienceandanalytics.aspx

Why choose this course?

- Big Data is now part of every sector and function of the global economy. Planning and strategic decision-making processes rely on large pools of data that need to be captured, aggregated, stored, and analysed.

- You will acquire both the foundational knowledge and the practical skills that prepare you for handling and analysing different types of data in different fields, thus responding to the needs of a huge variety of companies and organisations from retailers such as Tesco or Amazon, to manufacturers like BMW, health-care providers, or public administration. People with this set of skills are in short supply and high demand.

- You will have the opportunity to choose options among an exciting range of topics in Computer Science, Economics, Information Security, Management and Mathematics.

- You will also be well prepared to pursue studies at PhD level, which several companies prefer for their research laboratories and more advanced roles.

- Industry connections have informed the content and design of the course. External contacts in both academia and industry enrich the programme of seminars and guest lectures, which are an integral part of the course.

- Royal Holloway is located in the ‘M4 corridor’, west of London, a major high-technology hub also called ‘England’s Silicon Valley’.

- Royal Holloway is a very prestigious university in which to study. We are ranked not only as one of the 16 most beautiful universities in the world, but also one of the best: in 2012/13, the Times Higher Education World University Rankings placed the College 15th in the UK, 45th in Europe and 119th in the world.

Department research and industry highlights

- The excellence of our research in Machine Learning – the science behind ‘Big Data’ – is recognized worldwide, and the topics taught reflect that excellence.

- In the most recent Research Assessment Exercise (RAE 2008), the Department ranked 11th among UK Computer Science departments for its research output.

- The Department is ranked third in the UK for graduate employability by the Times Good University Guide 2013.

- The Department has an Industrial Liason Board that comprises senior representatives from Microsoft, Cognex, CSC, Bank of America Merrill Lynch, Kalido, Bathwick Group, Pentatonix, Blackrock, Oracle, Investec and QubeSoft.

Course content and structure

You will take taught modules during Term One (October to December) and Term Two (January to March). Examinations are held in May. You then take an industrial placement, after which you come back for your project/dissertation (12 weeks).
Please visit our websitefor additional information on this degree.

On completion of the course graduates will have:
- A highly analytical approach to problem solving.
- A strong background in data modelling and business intelligence.
- Knowledge of computational and statistical data analysis.
- A background in machine learning, statistics, and data mining.
- Ability to develop, validate, and use effectively machine learning models and statistical models.
- Ability to apply machine learning and data mining techniques to Information Retrieval and Natural Language Processing.
- Knowledge of and ability to work with software to automate tasks and perform data analysis.
- Knowledge of and ability to work with structured, unstructured, and time-series data.
- Ability to extract value and insight from data.
- Knowledge of and ability to work with methods and techniques such as clustering, regression, support vector machines, boosting, decision trees, neural networks.
- Appreciation and knowledge of non-statistical approaches to data analysis and machine learning.
- Ability to work with software packages such as MATLAB and R.
- Knowledge of and ability to work with relational databases (SQL), non-relational databases (mongodb), as well as with Hadoop/pig scripting and other big data manipulation techniques.
- Knowledge of and ability to work with Python, Perl, and Shell Scripting.

Assessment

Assessment is carried out by a variety of methods including coursework and a dissertation. The placement is assessed as part of your degree.

Employability & career opportunities

Our graduates are among the most employable in the UK – we rank third in the UK for graduate employability – and, in recent years, have entered many different Computer Science-related roles including network systems design and engineering, web development and production. Other graduates choose to enter careers with a management or financial slant. Our graduates have found employment at a wide range of organisations including Logica, British Telecom, British Aerospace, Microsoft, Amazon.com, American Express, Sky and Orbis Technology. At the same time, this course also equips you with a solid foundation for continued PhD studies.

Your careers ambitions are supported by our College Careers Service, located right next door to the Department. They offer application and interview coaching, career strategy discussions, and the opportunity to network with major employers on campus. Our careers service is provided by the Careers Group, the main provider of graduate recruitment services in London.

How to apply

Applications for entry to all our full-time postgraduate degrees can be made online https://www.royalholloway.ac.uk/studyhere/postgraduate/applying/howtoapply.aspx .

Read less
This degree, offered by the Department of Computer Science, will teach you both the foundational aspects and the practical skills that prepare you for handling… Read more
This degree, offered by the Department of Computer Science, will teach you both the foundational aspects and the practical skills that prepare you for handling and analysing different types of data in different fields, thus responding to the needs of a huge variety of companies and organisations, from retailers such as Tesco or Amazon, to manufacturers like BMW, to health-care providers, and to public administration.

As part of the course, you will take an industrial placement, where you will gain valuable experience by putting your knowledge and skills into practice.

See the website https://www.royalholloway.ac.uk/computerscience/coursefinder/mscdatascienceandanalytics(yearinindustry).aspx

Why choose this course?

- Big Data is now part of every sector and function of the global economy. Planning and strategic decision-making processes rely on large pools of data that need to be captured, aggregated, stored, and analysed.

- You will acquire both the foundational knowledge and the practical skills that prepare you for handling and analysing different types of data in different fields, thus responding to the needs of a huge variety of companies and organisations from retailers such as Tesco or Amazon, to manufacturers like BMW, health-care providers, or public administration. People with this set of skills are in short supply and high demand.

- You will have the opportunity to choose options among an exciting range of topics in Computer Science, Economics, Information Security, Management and Mathematics.

- You will also be well prepared to pursue studies at PhD level, which several companies prefer for their research laboratories and more advanced roles.

- Taking a placement is an excellent opportunity to gain industrial experience (which gives you an extra edge when applying for jobs in the future) and acquire skills that can only be fully picked up in a work environment.

- Industry connections have informed the content and design of the course. External contacts in both academia and industry enrich the programme of seminars and guest lectures, which are an integral part of the course.

- Royal Holloway is located in the ‘M4 corridor’, west of London, a major high-technology hub also called ‘England’s Silicon Valley’.

- Royal Holloway is a very prestigious university in which to study. We are ranked not only as one of the 16 most beautiful universities in the world, but also one of the best: in 2012/13, the Times Higher Education World University Rankings placed the College 15th in the UK, 45th in Europe and 119th in the world.

Department research and industry highlights

- The excellence of our research in Machine Learning – the science behind ‘Big Data’ – is recognized worldwide, and the topics taught reflect that excellence.

- In the most recent Research Assessment Exercise (RAE 2008), the Department ranked 11th among UK Computer Science departments for its research output.

- The Department is ranked third in the UK for graduate employability by the Times Good University Guide 2013.

- The Department has an Industrial Liaison Board that comprises senior representatives from Microsoft, Cognex, CSC, Bank of America Merrill Lynch, Kalido, Bathwick Group, Pentatonix, Blackrock, Oracle, Investec and QubeSoft.

Course content and structure

You will take taught modules during Term One (October to December) and Term Two (January to March). Examinations are held in May. You then take an industrial placement, after which you come back for your project/dissertation (12 weeks).

Your placement will take up to one year and, if you are an overseas student, your visa will cover the two years of the programme. The placement attracts a salary and is assessed as part of your degree. You will be assigned a supervisor by the host company, who is responsible for directing your work. You will be assigned an academic supervisor, who visits to check if you are integrating successfully and the type of work being undertaken is appropriate, and supports you in general during your placement. If you cannot or decide not to take a placement, you revert to the normal one-year degree.

On completion of the course graduates will have:
Throughout your degree, you will have the opportunity to acquire the following skills:

- A highly analytical approach to problem solving.
- A strong background in data modelling and business intelligence.
- Knowledge of computational and statistical data analysis.
- A background in machine learning, statistics, and data mining.
- Ability to develop, validate, and use effectively machine learning models and statistical models.
- Ability to apply machine learning and data mining techniques to Information Retrieval and Natural Language Processing.
- Knowledge of and ability to work with software to automate tasks and perform data analysis.
- Knowledge of and ability to work with structured, unstructured, and time-series data.
- Ability to extract value and insight from data.
- Knowledge of and ability to work with methods and techniques such as clustering, regression, support vector machines, boosting, decision trees, neural networks.
- Appreciation and knowledge of non-statistical approaches to data analysis and machine learning.
- Ability to work with software packages such as MATLAB and R.
- Knowledge of and ability to work with relational databases (SQL), non-relational databases (mongodb), as well as with Hadoop/pig scripting and other big data manipulation techniques.
- Knowledge of and ability to work with Python, Perl, and Shell Scripting.
- Work experience and appreciation of how your work fits into the organizational and development processes of a company.

Assessment

Assessment is carried out by a variety of methods including coursework, examinations and a dissertation. The placement is assessed as part of your degree.

Employability & career opportunities

Our graduates are among the most employable in the UK – we rank third in the UK for graduate employability – and, in recent years, have entered many different Computer Science-related roles including network systems design and engineering, web development and production. Other graduates choose to enter careers with a management or financial slant.

Our graduates have found employment at a wide range of organisations including Logica, British Telecom, British Aerospace, Microsoft, Amazon.com, American Express, Sky and Orbis Technology. At the same time, this course also equips you with a solid foundation for continued PhD studies.

Your careers ambitions are supported by our College Careers Service, located right next door to the Department. They offer application and interview coaching, career strategy discussions, and the opportunity to network with major employers on campus. Our careers service is provided by the Careers Group, the main provider of graduate recruitment services in London.

How to apply

Applications for entry to all our full-time postgraduate degrees can be made online https://www.royalholloway.ac.uk/studyhere/postgraduate/applying/howtoapply.aspx .

Read less
This degree, offered by the Department of Computer Science, allows you to develop a deeper understanding of Machine Learning – the science of systems that can learn from data – which companies such as Facebook, Google, Microsoft and Yahoo require to create, innovate, and define the next generation of search and analysis technologies. Read more
This degree, offered by the Department of Computer Science, allows you to develop a deeper understanding of Machine Learning – the science of systems that can learn from data – which companies such as Facebook, Google, Microsoft and Yahoo require to create, innovate, and define the next generation of search and analysis technologies.

See the website https://www.royalholloway.ac.uk/computerscience/coursefinder/mscmachinelearning.aspx

Why choose this course?

- Big Data is now part of every sector and function of the global economy. Planning and strategic decision-making processes rely on large pools of data that need to be captured, aggregated, stored, and analysed.

- You will gain in-depth knowledge and practical skills in Machine Learning techniques, which are used by companies such as Facebook, Google, Microsoft and Yahoo to develop the next generation of search and analysis technologies. People with this set of skills are in short supply and high demand.

- You will have the opportunity to choose options among an exciting range of topics in Computer Science, Economics, Information Security, Management and Mathematics.

- You will also be well prepared to pursue studies at PhD level, which several companies prefer for their research laboratories and more advanced roles.

- Industry connections have informed the content and design of the course. External contacts in both academia and industry enrich the programme of seminars and guest lectures, which are an integral part of the course.

- Royal Holloway is located in the ‘M4 corridor’, west of London, a major high-technology hub also called ‘England’s Silicon Valley’.

- Royal Holloway is a very prestigious university in which to study. We are ranked not only as one of the 16 most beautiful universities in the world, but also one of the best: in 2012/13, the Times Higher Education World University Rankings placed the College 15th in the UK, 45th in Europe and 119th in the world.

Department research and industry highlights

- The excellence of our research in Machine Learning is recognized worldwide, and the topics taught reflect that excellence.

- In the most recent Research Assessment Exercise (RAE 2008), the Department ranked 11th among UK Computer Science departments for its research output.

- The Department is ranked third in the UK for graduate employability by the Times Good University Guide 2013.

- The Department has an Industrial Liaison Board that comprises senior representatives from Microsoft, Cognex, CSC, Bank of America Merrill Lynch, Kalido, Bathwick Group, Pentatonix, Blackrock, Oracle, Investec and QubeSoft.

Course content and structure

You will take taught modules during Term One (October to December) and Term Two (January to March). Examinations are held in May. You then take an industrial placement, after which you come back for your project/dissertation (12 weeks).

On completion of the course graduates will have:
- A highly analytical approach to problem solving.
- A strong background in data modelling and business intelligence.
- Knowledge of computational and statistical data analysis.
- A background in machine learning, statistics, and data mining.
- Ability to develop, validate, and use effectively machine learning models and statistical models.
- Ability to apply machine learning and data mining techniques to Information Retrieval and Natural Language Processing.
- Knowledge of and ability to work with software to automate tasks and perform data analysis.
- Knowledge of and ability to work with structured, unstructured, and time-series data.
- Ability to extract value and insight from data.
- Knowledge of and ability to work with methods and techniques such as clustering, regression, support vector machines, boosting, decision trees, neural networks.
- Appreciation and knowledge of non-statistical approaches to data analysis and machine learning.
- Ability to work with software packages such as MATLAB and R.
- Knowledge of and ability to work with relational databases (SQL), non-relational databases (mongodb), as well as with Hadoop/pig scripting and other big data manipulation techniques.
- Knowledge of and ability to work with Python, Perl, and Shell Scripting.

Assessment

Assessment is carried out by a variety of methods including coursework and a dissertation. The placement is assessed as part of your degree.

Employability & career opportunities

Our graduates are among the most employable in the UK – we rank third in the UK for graduate employability – and, in recent years, have entered many different Computer Science-related roles including network systems design and engineering, web development and production. Other graduates choose to enter careers with a management or financial slant. Our graduates have found employment at a wide range of organisations including Logica, British Telecom, British Aerospace, Microsoft, Amazon.com, American Express, Sky and Orbis Technology. At the same time, this course also equips you with a solid foundation for continued PhD studies.

Your careers ambitions are supported by our College Careers Service, located right next door to the Department. They offer application and interview coaching, career strategy discussions, and the opportunity to network with major employers on campus. Our careers service is provided by the Careers Group, the main provider of graduate recruitment services in London.

How to apply

Applications for entry to all our full-time postgraduate degrees can be made online https://www.royalholloway.ac.uk/studyhere/postgraduate/applying/howtoapply.aspx .

Read less
This degree, offered by the Department of Computer Science, allows you to develop a deeper understanding of Machine Learning – the science of systems that can learn from data – which companies such as Facebook, Google, Microsoft and Yahoo require to create, innovate, and define the next generation of search and analysis technologies. Read more
This degree, offered by the Department of Computer Science, allows you to develop a deeper understanding of Machine Learning – the science of systems that can learn from data – which companies such as Facebook, Google, Microsoft and Yahoo require to create, innovate, and define the next generation of search and analysis technologies.

As part of the course, you will take an industrial placement, where you will gain valuable experience by putting your knowledge and skills into practice.

See the website https://www.royalholloway.ac.uk/computerscience/coursefinder/mscmachinelearning(yearinindustry).aspx

Why choose this course?

- Big Data is now part of every sector and function of the global economy. Planning and strategic decision-making processes rely on large pools of data that need to be captured, aggregated, stored, and analysed.

- You will gain in-depth knowledge and practical skills in Machine Learning techniques, which are used by companies such as Facebook, Google, Microsoft and Yahoo to develop the next generation of search and analysis technologies. People with this set of skills are in short supply and high demand.

- You will have the opportunity to choose options among an exciting range of topics in Computer Science, Economics, Information Security, Management and Mathematics.

- You will also be well prepared to pursue studies at PhD level, which several companies prefer for their research laboratories and more advanced roles.

- Taking a placement is an excellent opportunity to gain industrial experience (which gives you an extra edge when applying for jobs in the future) and acquire skills that can only be fully picked up in a work environment.

- Industry connections have informed the content and design of the course. External contacts in both academia and industry enrich the programme of seminars and guest lectures, which are an integral part of the course.

- Royal Holloway is located in the ‘M4 corridor’, west of London, a major high-technology hub also called ‘England’s Silicon Valley’.

- Royal Holloway is a very prestigious university in which to study. We are ranked not only as one of the 16 most beautiful universities in the world, but also one of the best: in 2012/13, the Times Higher Education World University Rankings placed the College 15th in the UK, 45th in Europe and 119th in the world.

Department research and industry highlights

- The excellence of our research in Machine Learning is recognized worldwide, and the topics taught reflect that excellence.

- In the most recent Research Assessment Exercise (RAE 2008), the Department ranked 11th among UK Computer Science departments for its research output.

- The Department is ranked third in the UK for graduate employability by the Times Good University Guide 2013.

- The Department has an Industrial Liaison Board that comprises senior representatives from Microsoft, Cognex, CSC, Bank of America Merrill Lynch, Kalido, Bathwick Group, Pentatonix, Blackrock, Oracle, Investec and QubeSoft.

Course content and structure

You will take taught modules during Term One (October to December) and Term Two (January to March). Examinations are held in May. You then take an industrial placement, after which you come back for your project/dissertation (12 weeks).

On completion of the course graduates will have:
- A highly analytical approach to problem solving.
- A strong background in data modelling and business intelligence.
- Knowledge of computational and statistical data analysis.
- A background in machine learning, statistics, and data mining.
- Ability to develop, validate, and use effectively machine learning models and statistical models.
- Ability to apply machine learning and data mining techniques to Information Retrieval and Natural Language Processing.
- Knowledge of and ability to work with software to automate tasks and perform data analysis.
- Knowledge of and ability to work with structured, unstructured, and time-series data.
- Ability to extract value and insight from data.
- Knowledge of and ability to work with methods and techniques such as clustering, regression, support vector machines, boosting, decision trees, neural networks.
- Appreciation and knowledge of non-statistical approaches to data analysis and machine learning.
- Ability to work with software packages such as MATLAB and R.
- Knowledge of and ability to work with relational databases (SQL), non-relational databases (mongodb), as well as with Hadoop/pig scripting and other big data manipulation techniques.
- Knowledge of and ability to work with Python, Perl, and Shell Scripting.
- Work experience and appreciation of how your work fits into the organizational and development processes of a company.

Assessment

Assessment is carried out by a variety of methods including coursework and a dissertation. The placement is assessed as part of your degree.

Employability & career opportunities

Our graduates are among the most employable in the UK – we rank third in the UK for graduate employability – and, in recent years, have entered many different Computer Science-related roles including network systems design and engineering, web development and production. Other graduates choose to enter careers with a management or financial slant. Our graduates have found employment at a wide range of organisations including Logica, British Telecom, British Aerospace, Microsoft, Amazon.com, American Express, Sky and Orbis Technology. At the same time, this course also equips you with a solid foundation for continued PhD studies.

Your careers ambitions are supported by our College Careers Service, located right next door to the Department. They offer application and interview coaching, career strategy discussions, and the opportunity to network with major employers on campus. Our careers service is provided by the Careers Group, the main provider of graduate recruitment services in London.

How to apply

Applications for entry to all our full-time postgraduate degrees can be made online https://www.royalholloway.ac.uk/studyhere/postgraduate/applying/howtoapply.aspx .

Read less
Do you want to affect the future of forests, a key natural resource and the wellspring of biodiversity? Have you ever wondered why forests are called the lungs of the Earth and how climate change relates to forests? Or how trees are grown and processed into products in a sustainable and efficient manner? And how are the economy and forests interrelated?. Read more
Do you want to affect the future of forests, a key natural resource and the wellspring of biodiversity? Have you ever wondered why forests are called the lungs of the Earth and how climate change relates to forests? Or how trees are grown and processed into products in a sustainable and efficient manner? And how are the economy and forests interrelated?

You can find answers to these questions when you study forest sciences. You will come to view forests not only as a setting for jogging trails or as a source of wood, but rather as a source of versatile renewable resources and as complex ecological systems that are closely connected to their environment. The relationship between humans and nature and between society and natural resources is a strong feature of these studies.

The Master’s Programme in Forest Sciences offers a broad and versatile perspective on forests and their use. The studies focus on and apply knowledge in biology, business economics, environmental sciences, logistics, geoinformatics and information technology. As a graduate in forest sciences you will be a professional in forest ecology, the management and use of forest resources, forest bioeconomy business and policy, with ample career opportunities in Finland and abroad.

Come and study forest sciences at the University of Helsinki, in one of the world’s foremost degree programmes in the field. For more information in Finnish about studies in forest sciences, the field of forestry and its opportunities, see http://www.metsatieteet.fi.

The University of Helsinki will introduce annual tuition fees to foreign-language Master’s programmes starting on August 1, 2017 or later. The fee ranges from 13 000-18 000 euros. Citizens of non-EU/EEA countries, who do not have a permanent residence status in the area, are liable to these fees. You can check this FAQ at the Studyinfo website whether or not you are required to pay tuition fees: https://studyinfo.fi/wp2/en/higher-education/higher-education-institutions-will-introduce-tuition-fees-in-autumn-2017/am-i-required-to-pay-tuition-fees/

Programme Contents

General studies in the Master’s programme provide you with skills needed for the academic world and the labour market. In advanced studies, you focus on field-specific issues and develop your professional knowledge when writing your Master’s thesis and completing courses in your field of specialisation. In addition, the studies include elective courses that allow you to diversify and deepen your knowledge.

The Master's Programme in Forest Sciences comprises three study tracks: forest ecology, the management and use of forest resources and forest bioeconomy business and policy. These study tracks include a total of 12 fields of specialisation.

The specialisations in forest ecology focus on various types of forest and peatland ecosystems and their exploitation. Topical issues include climate change, the prevention of damage to forests caused by insects and fungi, the control of game populations, and problems related to the exploitation of tropical forests.

The specialisations in the management and use of forest resources examine the planning of forest use and the relevant collection of information, forest inventory models, wood harvesting and logistics as well as the processing of wood into bioeconomy products. Topical issues include the application of new remote sensing methods in the planning of forest resource management, the combination of different values and targets in forestry and bioeconomy, various models of silviculture, increased efficiency in logging and transportation, and generating added value in all areas of biorefining.

Studies in the business economics of forest bioeconomy are based on the sustainable use of a renewable natural resource and on the development of responsible business activities in a global environment. The focus of studies is on the globalisation of forest-based industry and business and its structural redevelopment into the bioeconomy. You will become familiar with forest-based issues of the bioeconomy in production, marketing and policy as part of the global operating environment.

Selection of the Major

Graduates from the Bachelor’s Programme in Forest Sciences at the University of Helsinki can continue their studies in the Master's Programme in Forest Sciences. There is an application process for graduates from other Bachelor’s programmes, from universities of applied sciences, and for international applicants.

In the application process, you are selected for the Master’s Programme in Forest Sciences. Upon admission you must select one of the three study tracks, and you must select your specialisation by the second year of your Master’s studies.

Study tracks, specialisations and examples of topics covered by them:
Forest Ecology
-The management and restoration of forest ecosystems: the sustainable and multitargeted use of forest, the use of peat.
-Forest soil science: the biogeochemistry and hydrology of forest soil, soil and root ecology.
-Forest pathology and mycology: the microbiology and epidemiology of forests.
-Forest zoology: the biology and ecology of forest insects, the ecology of forest pests.
-Wildlife management: game populations and society, the planning of game husbandry, mammal ecology.
-The ecology, management and use of tropical forests: methods of tropical forestry, agroforestry.

Management and Use of Forest Resources
-Forest resource management: the collection and use of forest-related information in decision-making, laser scanning, remote sensing, forest inventory.
-Forest technology and logistics: the management of forest products, terramechanics, forest bioenergy.
-Wood technology: wood science and wood as raw material, laboratories in the forest industry, the structure and properties of wood raw material.

Forest Bioeconomy Business and Policy
-Marketing and management in the forest industry: strategic management and marketing, responsibility in forestry, customer orientation, innovations.
-Forest economics: business economics of units within forest bioeconomy, economics of silviculture, forest investment and the economic impact of environmental targets.
-International forest policy: global processes and trends impacting the forest sector from the perspective of individuals, communities and nations.

Programme Structure

The Bachelor’s Programme in Forest Sciences includes two study tracks: forest ecology and the use of forest resources, and forest economics and marketing. The Master's Programme in Forest Sciences comprises three study tracks: forest ecology, the management and use of forest resources, and business economics and policies of forest bioeconomy. These study tracks include a total of 12 specialisations (see specialisations above). Upon completing the Master's Programme in Forest Sciences you will be eligible to apply for the Doctoral Programme in Sustainable Use of Renewable Natural Resources.

Career Prospects

A degree in forestry offers extensive and fairly unique professional competence on a global scale on forest and peatland ecosystems, forest management and use, forest conservation, the business economics and policies of forest bioeconomy as well as the collection, management and use of forest-related information. For more information in Finnish on the available career opportunities, see http://www.metsatieteet.fi

Internationalization

Studies in forestry offer ample opportunities for international activities. For example, you can complete your practical training or collect material for your Master’s thesis abroad. Most courses in the Master’s programme are in English, and several international students participate. You can also serve as a tutor for international exchange students and establish contacts and networks in this way. Another example of international activities is the Helsinki Summer School, which offers intensive courses on topical issues and brings together students from as many as 60 countries.

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Sustainability Engineering concerns the responsible use of natural resources in a manner which does not compromise the ability of future generations to meet their own needs. Read more
Sustainability Engineering concerns the responsible use of natural resources in a manner which does not compromise the ability of future generations to meet their own needs. This course addresses the engineering technologies and practices needed to efficiently exploit energy and materials, to mitigate the environmental impact, and to apply concepts such as life-cycle analysis, disassembly and recycling within the design process.

The course provides the necessary skills to analyse and assess sustainability issues, and to formulate strategies to implement practical sustainable development solutions in business.

In the MSc dissertation, students either conduct “in house” laboratory based projects or are placed in an industrial or commercial setting where they undertake environmental management system assessments or technical design analyses. Previous MSc student dissertations involved collaboration or placements with Alchema Ltd., Frames Separation Technologies, Caledonian Alloys, Senertec GmbH, United Wire Ltd., Scottish Whisky Research Institute, Nextek Ltd., Forestry Commission and the Scottish Environmental Protection Agency.

About Heriot-Watt:

Heriot-Watt University is set in almost 400 acres of woodland, making it one of the most beautiful places to study and live. Less than ten miles and a 15-minute bus ride into the centre of Edinburgh, its proximity to this architecturally famous city only adds to its appeal.

Edinburgh is renowned as a centre of learning and discovery; studying and living here is a stimulating and inspirational experience. It offers a unique city environment. It's culturally diverse, historically significant, socially alive, environmentally aware, politically central and visually stunning. The centre of Edinburgh has been awarded UNESCO World Heritage Site status in recognition of its stunning urban landscape in the medieval Old Town and the Georgian New Town. In addition, the city encompasses some striking modern architecture including the parliament building and the Museum of Scotland.

To top it all, Edinburgh is lucky to have significant and magnificent green spaces: the impressive extinct volcano, Arthur's Seat, and its associated park, lie at the heart of the city. If that wasn't enough, Edinburgh has 112 public parks and more trees per person than any other British city.

You're also within easy striking distance of some of the world's most beautiful wild landscapes, from the Trossachs in the west and the Highlands in the north, to the Borders in the south.

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visit course pages for more information about the next Open Day at NHM on Wednesday 29 March 2017. Taxonomy and systematics provide the foundation for studying the great diversity of the living world. Read more

Open Day

visit course pages for more information about the next Open Day at NHM on Wednesday 29 March 2017.

Course Overview

Taxonomy and systematics provide the foundation for studying the great diversity of the living world. These fields are rapidly changing through new digital and molecular technologies. There is ever greater urgency for species identification and monitoring in virtually all the environmental sciences, and evolutionary ‘tree thinking’ is now applied widely in most areas of the life sciences.

This course provides in-depth training in the study of biodiversity based on the principles of phylogenetics, evolutionary biology, palaeobiology and taxonomy. The emphasis is on quantitative approaches and current methods in DNA-based phylogenetics, bioinformatics, and the use of digital collections.

Location

This course is a collaboration of Imperial College London (Silwood Park) with the Natural History Museum. This provides an exciting scientific environment of two institutions at the forefront of taxonomic and evolutionary research.

The MSc in Taxonomy and Biodiversity comprises two terms of taught modules, mostly based at the Natural History Museum, and covers core areas in biodiversity, palaeobiology, phylogenetics, molecular systematics, phylogenomics and taxonomic principles. This is followed by a 16-week laboratory or field-based research project at the NHM or Imperial College’s Silwood Park or South Kensington campuses.

Modules

• Taxonomy of major groups and the Tree-of-Life: An introduction of major branches of the Tree, including identification exercises, presented by NHM experts
• Statistics and Computing: A two-week intensive course at Silwood Park
• Field course: trapping and collecting techniques for terrestrial and aquatic ecosystems
• Phylogenetic Reconstruction: the principles of building phylogenetic trees
• Molecular Systematics: generating and analysing molecular data; model-based phylogenetics
• Phylogenomics: Genomic techniques for studying evolutionary processes and biodiversity
• Biodiversity (Concepts): speciation, radiation, macroevolution
•Biodiversity (Applied): Measuring biodiversity, geospatial analysis, collection management and biodiversity informatics
• Palaeobiology: Studying the fossil record and what we can learn for biodiversity

Post Study

Students on the course will become the new generation of taxonomists in the broadest sense. They will be familiar with these new tools, as well as the wider concepts of biodiversity science, evolutionary biology and genomics. Most importantly, students gain the abilities to work as an independent scientist and researcher, to be able to solve questions about the future of biodiversity and to communicate them to peers and the public.
Students have many options for future employment in evolutionary and ecological research labs in industry, government and non-governmental organisations, conservation, and scientific publishing and the media. The courses are an excellent starting point for PhD level careers, feeding into various Doctoral Training Programmes available at NHM and Imperial, or elsewhere.

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The MSc in Bioinformatics and Computational Biology at UCC is a one-year taught masters course commencing in September. Bioinformatics is a fast-growing field at the intersection of biology, mathematics and computer science. Read more
The MSc in Bioinformatics and Computational Biology at UCC is a one-year taught masters course commencing in September. Bioinformatics is a fast-growing field at the intersection of biology, mathematics and computer science. It seeks to create, advance and apply computer/software-based solutions to solve formal and practical problems arising from the management and analysis of very large biological data sets. Applications include genome sequence analysis such as the human genome, the human microbiome, analysis of genetic variation within populations and analysis of gene expression patterns.

As part of the MSc course, you will carry out a three month research project in a research group in UCC or in an external university, research institute or industry. The programming and data handling skills that you will develop, along with your exposure to an interdisciplinary research environment, will be very attractive to employers. Graduates from the MSc will have a variety of career options including working in a research group in a university or research institute, industrial research, or pursuing a PhD.

Visit the website: http://www.ucc.ie/en/ckr33/

Course Detail

This MSc course will provide theoretical education along with practical training to students who already have a BSc in a biological/life science, computer science, mathematics, statistics, engineering or a related degree.

The course has four different streams for biology, mathematics, statistics and computer science graduates. Graduates of related disciplines, such as engineering, physics, medicine, will be enrolled in the most appropriate stream. This allows graduates from different backgrounds to increase their knowledge and skills in areas in which they have not previously studied, with particular emphasis on hands-on expertise relevant to bioinformatics:

- Data analysis: basic statistical concepts, probability, multivariate analysis methods
- Programming/computing: hands-on Linux skills, basic computing skills and databases, computer system organisation, analysis of simple data structures and algorithms, programming concepts and practice, web applications programming
- Bioinformatics: homology searches, sequence alignment, motifs, phylogenetics, protein folding and structure prediction
- Systems biology: genome sequencing projects and genome analysis, functional genomics, metabolome modelling, regulatory networks, interactome, enzymes and pathways
- Mathematical modelling and simulation: use of discrete mathematics for bioinformatics such as graphs and trees, simulation of biosystems
- Research skills: individual research project, involving a placement within the university or in external research institutes, universities or industry.

Format

Full-time students must complete 12 taught modules and undertake a research project. Part-time students complete about six taught modules in each academic year and undertake the project in the second academic year. Each taught module consists of approximately 20 one-hour lectures (roughly two lectures per week over one academic term), as well as approximately 10 hours of practicals or tutorials (roughly one one-hour practical or tutorial per week over one academic term), although the exact amount of lectures, practicals and tutorials varies between individual modules.

Assessment

There are exams for most of the taught modules in May of each of the two academic years, while certain modules may also have a continuous assessment element. The research project starts in June and finishes towards the end of September. Part-time students will carry out their research project during the summer of their second academic year.

Careers

Graduates of this course offer a unique set of interdisciplinary skills making them highly attractive to employers at universities, research centres and in industry. Many research institutes have dedicated bioinformatics groups, while many 'wet biology' research groups employ bioinformaticians to help with data analyses and other bioinformatics problems. Industries employing bioinformaticians include the pharmaceutical industry, agricultural and biotechnology companies. For biology graduates returning to 'wet lab' biology after completing the MSc course, your newly acquired skills will be extremely useful. Non-biology graduates seeking non-biology positions will also find that having acquired interdisciplinary skills is of great benefit in getting a job.

How to apply: http://www.ucc.ie/en/study/postgrad/how/

Funding and Scholarships

Information regarding funding and available scholarships can be found here: https://www.ucc.ie/en/cblgradschool/current/fundingandfinance/fundingscholarships/

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Designed for aspiring finance professionals who want to gain specialist knowledge, this course will equip you with the skills required for seeking employment in the increasingly complex and demanding financial services sector. Read more
Designed for aspiring finance professionals who want to gain specialist knowledge, this course will equip you with the skills required for seeking employment in the increasingly complex and demanding financial services sector.

You will explore the theories and current practice in the finance industry from both a national and international perspective. The course blends theory with practice to provide you with the applied vocational skills that potential employers require.

You'll study in the heart of Leeds, which is the largest hub of financial services in the UK outside London. Leeds is home to many leading financial organisations and large professional services firms. You'll be taught by a range of highly experienced tutors with industrial, professional and academic knowledge.

We include a strong international dimension in the course content and attract students from as far and wide as China, India, Pakistan and Vietnam.

Faculty of Business & Law website (https://www.leedsmet.ac.uk/fbl/)
Request a call back (http://www.leedsmet.ac.uk/study/postgraduate.htm)

January entrants please note: in order to complete 12 months of academic study delivered in University term time, the total length of your programme will be 18 months to include recognised University vacation periods.

- Research Excellence Framework 2014: twice as many of our staff - 220 - were entered into the research assessment for 2014 compared to the number entered in 2008.

Visit the website http://courses.leedsbeckett.ac.uk/finance_msc

Mature Applicants

Our University welcomes applications from mature applicants who demonstrate academic potential. We usually require some evidence of recent academic study, for example completion of an access course, however recent relevant work experience may also be considered. Please note that for some of our professional courses all applicants will need to meet the specified entry criteria and in these cases work experience cannot be considered in lieu.

If you wish to apply through this route you should refer to our University Recognition of Prior Learning policy that is available on our website (http://www.leedsbeckett.ac.uk/studenthub/recognition-of-prior-learning.htm).

Please note that all applicants to our University are required to meet our standard English language requirement of GCSE grade C or equivalent, variations to this will be listed on the individual course entry requirements.

Careers

This course has a proven track record of graduates gaining employment in the financial services sector, such as banking or insurance. Others have entered into their family business, taking on a variety of management level roles. A small number have remained in the education sector to study a PhD and have entered into the teaching profession.

- Financial Analyst
- Financial Advisor
- Director of Finance
- Chief Financial Officer

Careers advice:
The dedicated Jobs and Careers team offers expert advice and a host of resources to help you choose and gain employment. Whether you're in your first or final year, you can speak to members of staff from our Careers Office who can offer you advice from writing a CV to searching for jobs.

Visit our careers site - https://www.leedsbeckett.ac.uk/employability/jobs-careers-support.htm

Course Benefits

Our MSc Finance is delivered in the heart of Leeds, which is the largest hub of financial services in the UK outside London and which is home to many leading financial organisations and large professional services firms.

The course is delivered by a range of highly experienced tutors with industrial, professional and academic experience. The course emphasises blending theory with practice and thus provides students with the applied vocational skills that potential employers require. It is international both in terms of content and the student mix which provides a multicultural learning environment.

At Leeds Business School we're dedicated to supporting your professional development - that's why we offer a guest lecture programme. Past speakers include the CEO of the London Stock Exchange, Shadow Chief Secretary to the Treasury, past Chair and President of the Academy of Marketing, Chief Executive of the British Bankers Association, the Chief Economist of Yorkshire Bank and the Editor of Cosmopolitan. To see our full programme and to register for a lecture click here (http://www.leedsmet.ac.uk/guestspeakers).

Core Modules

Corporate Finance
Evaluate the fundamental concepts and theories of modern finance, identifying how these can be effectively applied in both national and multinational organisations.

Financial Decision Analysis
Cover topics on decision theory (decision trees and tables), linear programming, regression, time series, portfolio optimisation, discounted cash flow and finance.

Financial Economics
Gain a comprehensive economic analysis of the operation, efficiency and dependencies between financial markets and their associated institutions. You will also assess the impact upon the world economy of any failure of the financial sector.

Managing Financial Resources
Gain a critical understanding of contemporary accounting and financing principles which support business decision-making and financial resourcing in both the private and public sectors.

Understanding the Economy
Develop knowledge and a critical understanding of the workings of a major economy which is subject to a constantly changing global environment, de-regulated financial systems, modern mass communication and international monetary flows.

Dissertation
You will carry out an in-depth research project in a subject area that is appropriate to the course and of particular interest to you.

Option Modules

Forensic Accounting
Discover the need for and role of corporate governance in the business environment, the role of IT in forensic accounting and fraud detection and the types and incidences of fraud.

Investment Fund Management
You will identify, understand, evaluate and compare types of investment for the private investor, including tools, methods and strategies.

Management of International Finance
Gain a comprehensive understanding of the economics of the operation and organisation of national and international financial systems.

"We are proud of the success of our national and international graduates."
- Professor Christopher Prince
Dean and Pro Vice Chancellor of the Faculty of Business and Law

Facilities

- Library
Our libraries are two of the only university libraries in the UK open 24/7 every day of the year. However you like to study, the libraries have got you covered with group study, silent study, extensive e-learning resources and PC suites.

- The Rose Bowl
The Rose Bowl has impressive teaching spaces, auditoriums, conference facilities and an outstanding local reputation as a business hub. The Rose Bowl puts our students at the centre of a dynamic business community.

Find out how to apply here - http://www.leedsbeckett.ac.uk/postgraduate/how-to-apply/

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