Masters Degrees (Computational)

Take advantage of one of our 100 Master’s Scholarships to study Computational Mechanics at Swansea University, the Times Good University Guide’s Welsh University of the Year 2017. Postgraduate loans are also available to English and Welsh domiciled students. For more information on fees and funding please visit our website.

Swansea University has gained a significant international profile as one of the key international centres for research and training in computational mechanics and engineering. As a student on the Master's course in Erasmus Mundus Computational Mechanics, you will be provided with in-depth, multidisciplinary training in the application of the finite element method and related state-of-the-art numerical and computational techniques to the solution and simulation of highly challenging problems in engineering analysis and design.

Key Features of Erasmus Mundus Computational Mechanics MSc

The Zienkiewicz Centre for Computational Engineering is acknowledged internationally as the leading UK centre for computational engineering research. It represents an interdisciplinary group of researchers who are active in computational or applied mechanics. It is unrivalled concentration of knowledge and expertise in this field. Many numerical techniques currently in use in commercial simulation software have originated from Swansea University.

The Erasmus Mundus MSc Computational Mechanics course is a two-year postgraduate programme run by an international consortium of four leading European Universities, namely Swansea University, Universitat Politècnica de Catalunya (Spain), École Centrale de Nantes (France) and University of Stuttgart (Germany) in cooperation with the International Centre for Numerical Methods in Engineering (CIMNE, Spain).

As a student on the Erasmus Mundus MSc Computational Mechanics course, you will gain a general knowledge of the theory of computational mechanics, including the strengths and weaknesses of the approach, appreciate the worth of undertaking a computational simulation in an industrial context, and be provided with training in the development of new software for the improved simulation of current engineering problems.

In the first year of the Erasmus Mundus MSc Computational Mechanics course, you will follow an agreed common set of core modules leading to common examinations in Swansea or Barcelona. In addition, an industrial placement will take place during this year, where you will have the opportunity to be exposed to the use of computational mechanics within an industrial context. For the second year of the Erasmus Mundus MSc Computational Mechanics, you will move to one of the other Universities, depending upon your preferred specialisation, to complete a series of taught modules and the research thesis. There will be a wide choice of specialisation areas (i.e. fluids, structures, aerospace, biomedical) by incorporating modules from the four Universities. This allows you to experience postgraduate education in more than one European institution.

Modules

Modules on the Erasmus Mundus MSc Computational Mechanics course can vary each year but you could expect to study the following core modules (together with elective modules):

Numerical Methods for Partial Differential Equations
Continuum Mechanics
Advanced Fluid Mechanics
Industrial Project
Finite Element Computational Analysis
Entrepreneurship for Engineers
Finite Element in Fluids
Computational Plasticity
Fluid-Structure Interaction
Nonlinear Continuum Mechanics
Computational Fluid Dynamics
Dynamics and Transient Analysis
Reservoir Modelling and Simulation

Accreditation

The Erasmus Mundus Computational Mechanics course is accredited by the Joint Board of Moderators (JBM).

The Joint Board of Moderators (JBM) is composed of the Institution of Civil Engineers (ICE), the Institution of Structural Engineers (IStructE), the Chartered Institution of Highways and Transportation (CIHT), and the Institute of Highway Engineers (IHE).

This degree is accredited as meeting the requirements for Further Learning for a Chartered Engineer (CEng) for candidates who have already acquired an Accredited CEng (Partial) BEng(Hons) or an Accredited IEng (Full) BEng/BSc (Hons) undergraduate first degree.

See http://www.jbm.org.uk for further information.

This degree has been accredited by the JBM under licence from the UK regulator, the Engineering Council.

Accreditation is a mark of assurance that the degree meets the standards set by the Engineering Council in the UK Standard for Professional Engineering Competence (UK-SPEC). An accredited degree will provide you with some or all of the underpinning knowledge, understanding and skills for eventual registration as an Incorporated (IEng) or Chartered Engineer (CEng). Some employers recruit preferentially from accredited degrees, and an accredited degree is likely to be recognised by other countries that are signatories to international accords.

Links with Industry

On the Erasmus Mundus MSc Computational Mechanics course, you will have the opportunity to apply your skills and knowledge in computational mechanics in an industrial context.

As a student on the Erasmus Mundus MSc Computational Mechanics course you will be placed in engineering industries, consultancies or research institutions that have an interest and expertise in computational mechanics. Typically, you will be trained by the relevant industry in the use of their in-house or commercial computational mechanics software.

You will also gain knowledge and expertise on the use of the particular range of commercial software used in the industry where you are placed.

Careers

The next decade will experience an explosive growth in the demand for accurate and reliable numerical simulation and optimisation of engineering systems.

Computational mechanics will become even more multidisciplinary than in the past and many technological tools will be, for instance, integrated to explore biological systems and submicron devices. This will have a major impact in our everyday lives.

Employment can be found in a broad range of engineering industries as this course provides the skills for the modelling, formulation, analysis and implementation of simulation tools for advanced engineering problems.



Student Quotes

“I gained immensely from the high quality coursework, extensive research support, confluence of cultures and unforgettable friendship.”

Prabhu Muthuganeisan, MSc Computational Mechanics

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Research profile

The Institute for Adaptive and Neural Computation (IANC) is a world-leading institute dedicated to the theoretical and empirical study of adaptive processes in both artificial and biological systems. We are one of the UK’s largest and most prestigious academic teams in these fields.

We foster world-class interdisciplinary and collaborative research bringing together a range of disciplines.

Our research falls into three areas:

-machine learning
-computational neuroscience
-computational biology

In machine learning we develop probabilistic methods that find patterns and structure in data, and apply them to scientific and technological problems. Applications include areas as diverse as astronomy, health sciences and computing.

In computational neuroscience and neuroinformatics we study how the brain processes information, and analyse and interpret data from neuroscientific experiments

The focus in the computational biology area is to develop computational strategies to store, analyse and model a variety of biological data (from protein measurements to insect behavioural data).

Training and support

You carry out your research within a research group under the guidance of a supervisor. You will be expected to attend seminars and meetings of relevant research groups and may also attend lectures that are relevant to your research topic. Periodic reviews of your progress will be conducted to assist with research planning.

A programme of transferable skills courses facilitates broader professional development in a wide range of topics, from writing and presentation skills to entrepreneurship and career strategies.

The School of Informatics holds a Silver Athena SWAN award, in recognition of our commitment to advance the representation of women in science, mathematics, engineering and technology. The School is deploying a range of strategies to help female staff and students of all stages in their careers and we seek regular feedback from our research community on our performance.

Facilities

The award-winning Informatics Forum is an international research facility for computing and related areas. It houses more than 400 research staff and students, providing office, meeting and social spaces.

It also contains two robotics labs, an instrumented multimedia room, eye-tracking and motion capture systems, and a full recording studio amongst other research facilities. Its spectacular atrium plays host to many events, from industry showcases and student hackathons to major research conferences.

Nearby teaching facilities include computer and teaching labs with more than 250 machines, 24-hour access to IT facilities for students, and comprehensive support provided by dedicated computing staff.

Among our entrepreneurial initiatives is Informatics Ventures, set up in 2008 to support globally ambitious software companies in Scotland and nurture a technology cluster to rival Boston, Pittsburgh, Kyoto and Silicon Valley.

Career opportunities

The research you will undertake at IANC is perfectly suited to a career in academia, where you’ll be able to use your knowledge to advance this important field. Some graduates take their skills into commercial research posts, and find success in creating systems that can be used in everyday applications.

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Graduate education in Computational Science and Engineering (CMSE) at Koç University is offered through an interdisciplinary program among the Departments of the College of Arts and Sciences and the College of Engineering. In this program graduate students are trained on modern computational science techniques and their applications to solve scientific and engineering problems. New technological problems and associated research challenges heavily depend on computational modeling and problem solving. Because of the availability of powerful and inexpensive computers model-based computational experimentation is now a standard approach to analysis and design of complex systems where real experiments can be expensive or infeasible. Graduates of the CMSE Program should be capable of formulating solutions to computational problems through the use of multidisciplinary knowledge gained from a combination of classroom and laboratory experiences in basic sciences and engineering. Individuals with B.S. degrees in biology, chemistry, physics, and related engineering disciplines should apply for graduate study in the CMSE Program.

Current faculty projects and research interests:

• Computational Biology & Bioinformatics
• Computational Chemistry
• Computational Physics
• Molecular Dynamics and Simulation
• Parallel and High Performance Computing
• Computational Fluid Dynamics
• Dynamical and Stochastic Systems
• Quantum Mechanics of Many Body Systems
• Electronic Design Automation
• Numerical Methods
• Simulation of Material Synthesis
• Structural Dynamics
• Biomedical Modeling and Simulation
• Virtual Environments

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Our MSc Computational Finance equips you with the core concepts and mathematical principles of modern quantitative finance, plus the operational skills to use computational packages (mainly Matlab) for financial modelling.

We provide practical, hands-on learning about how modern, highly computerised financial markets work, how assets should be priced, and how investors should construct a portfolio of assets. In addition to traditional topics in derivatives and asset pricing, we place a special emphasis on risk management in non-Gaussian environment with extreme events.

You master these areas through studying topics including:
-Non-linear and evolutionary computational methods for derivatives pricing and portfolio management
-Applications of calculus and statistical methods
-Computational intelligence in finance and economics
-Financial markets

You also graduate with an understanding of the use of artificial financial market environments for stress testing, and the design of auctions and other financial contracts.

Our Centre for Computational Finance and Economic Agents is an innovative and laboratory-based teaching and research centre, with an international reputation for leading-edge, interdisciplinary work combining economic and financial modelling with computational implementation.

Our research is geared towards real-world, practical applications, and many of our academic staff have experience of applying their findings in industry and in advising the UK government.

This course is also available on a part-time basis.

Professional accreditation

This degree is accredited by the Institution of Engineering and Technology (IET).This accreditation is increasingly sought by employers, and provides the first stage towards eventual professional registration as a Chartered Engineer (CEng).

Our expert staff

This course is taught by experts with both academic and industrial expertise in the financial and IT sectors. We bring together leading academics in the field from our departments of economics, computer science and business.

Our staff are currently researching the development of real-time trading platforms, new financial econometric models for real-time data, the use of artificially intelligent agents in the study of risk and market-based institutions, operational aspects of financial markets, financial engineering, portfolio and risk management.

Specialist facilities

We are one of the largest and best resourced computer science and electronic engineering schools in the UK. Our work is supported by extensive networked computer facilities and software aids, together with a wide range of test and instrumentation equipment.
-We have six laboratories that are exclusively for computer science and electronic engineering students. Three are open 24/7, and you have free access to the labs except when there is a scheduled practical class in progress
-All computers run either Windows 7 or are dual boot with Linux
-Software includes Java, Prolog, C++, Perl, Mysql, Matlab, DB2, Microsoft Office, Visual Studio, and Project
-Students have access to CAD tools and simulators for chip design (Xilinx) and computer networks (OPNET)
-We also have specialist facilities for research into areas including non-invasive brain-computer interfaces, intelligent environments, robotics, optoelectronics, video, RF and MW, printed circuit milling, and semiconductors

Your future

We have an extensive network of industrial contacts through our City Associates Board and our alumni, while our expert seminar series gives you the opportunity to work with leading figures from industry.

Our recent graduates have gone on to become quantitative analysts, portfolio managers and software engineers at various institutions, including:
-HSBC
-Mitsubishi UFJ Securities
-Old Mutual
-Bank of England

We also work with the university’s Employability and Careers Centre to help you find out about further work experience, internships, placements, and voluntary opportunities.

Example structure

-CCFEA MSc Dissertation
-Financial Engineering and Risk Management
-Introduction to Financial Market Analysis
-Learning and Computational Intelligence in Economics and Finance
-Professional Practice and Research Methodology
-Quantitative Methods in Finance and Trading
-Big-Data for Computational Finance (optional)
-Industry Expert Lectures in Finance (optional)
-Mathematical Research Techniques Using Matlab (optional)
-Programming in Python (optional)
-Artificial Neural Networks (optional)
-High Frequency Finance and Empirical Market Microstructure (optional)
-Machine Learning and Data Mining (optional)
-Trading Global Financial Markets (optional)
-Creating and Growing a New Business Venture (optional)
-Evolutionary Computation and Genetic Programming (optional)
-Constraint Satisfaction for Decision Making (optional)

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This studio based program develops your arts practice through the expressive world of creative computation. It provides you with the historical foundations, frameworks and critical skills to produce a series of projects for public exhibition. It is delivered by Computing with contributions from the Centre for Cultural Studies- http://www.gold.ac.uk/pg/mfa-computational-arts/

What is computational art?

Computation consists of all the changes brought about by digital technology. Art is an open set of ways of acting inventively in culture. Mixing the two together in a systematic way gives us computational art. This is a very open field, and one that is set to expand enormously in the coming years. It is where the most exciting developments in technology and in culture can already be found. This degree will place you in the middle of this fast-evolving context.

Follow the links in the student profiles section for work produced by our graduates

What will I learn?

This degree develops your arts practice through the expressive world of creative computation. Over a two years (full-time) or four years (part-time) you will develop your artistic work and thinking through the challenge of developing a series of projects for public exhibition which will explore the technological and cultural ramifications of computation.

You will learn the fundamentals of programming and how to apply this knowledge expressively. You will work with popular open source programming environments such as Processing, OpenFrameworks, P5.js and Arduino, and will learn how to program in languages such as Java, Javascript and C++.

Since computational artworks don’t necessarily involve computers and screens, we also encourage students to produce works across a diverse range of media. Supported by studio technicians in state-of-the-art facilities, our students are producing works using tools such as 3D printers, laser cutters, robotics, wearable technologies, paint, sculpture and textiles.

You will also study contextual modules on computational art and the socio-political effects of technology. Modules in the Centre for Cultural Studies provide students with the historical foundations, frameworks, critical skills and confidence to express their ideas effectively. You will have the opportunity to learn the cultural histories of technology, to reflect on computation in terms of its wider cultural effects, and to understand the way in which art provides rigorous ways of thinking.

Through our masterclass series, we regularly invite world-class artists and curators to explain their work and engage in critical dialogue with the students. This allows you to develop a wider understanding of the contemporary art scene and how your work sits within the professional art world.

Contact the department

If you have specific questions about the degree, contact Theo Papatheodorou.

Modules & Structure

Year 1 shares the same core learning as our MA in Computational Arts programme:

Programming for Artists 1- 15 credits
Programming for Artists 2- 15 credits
Workshops in Creative Coding 1- 15 credits
Final Project in Computational Arts- 60 credits
Physical Computing
Interactive Media Critical Theory- 15 or 30 credits
Physical Computing: Arduino and Related Technologies- 30 credits

In Year 2 you will study the following:

Studio Practice- 120 credits
Computational Arts Critical Studies- 60 credits

Assessment

In Year 2 you will be assessed by: self-evaluation report of 2,500 words; essay of up to 6,000 words; viva voce; exhibition of final work.

Skills & Careers

The programme will equip you with a broad training in the use of creative computing systems that are currently most important in artistic, design and cultural practices and the creative industries, as well as technologies that are yet to emerge.

Funding

Please visit http://www.gold.ac.uk/pg/fees-funding/ for details.

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Programme description

The MSc in Computational Mathematical Finance (CMF) is a dynamic new programme with the aim to deliver high quality training in the theory of Mathematical Finance with strong emphasis on computational methods.

Currently graduates in this field are expected to have a working knowledge of advanced computational finance (including construction of algorithms and programming skills) as well as a sound knowledge of the theory of Probability and Stochastic Analysis. These are the core theories needed in the modern valuation of complex financial instruments.

This MSc programme delivers:

• a flexible programme of study relevant to the needs of employers such as: top investment banks, hedge funds and asset management firms
• a solid knowledge in financial derivative pricing, risk management and portfolio management
• the transferable computational skills required by the modern quantitative finance world

Programme structure

You must obtain a total of 180 credits to be awarded the MSc. Over semesters 1 and 2, you will take compulsory courses worth a total of 85 credits and optional courses worth a further 35 credits. Successful performance in these courses (assessed through coursework or examinations or both) allows you to start work on a three-month dissertation project, worth 60 credits, for the award of the MSc degree.

There are two streams: the Financial stream and the Computational stream.

Compulsory courses (both streams):

• Stochastic Analysis in Finance (20 credits, semester 1)
• Discrete-Time Finance (10 credits, semester 1)
• Finance, Risk and Uncertainty (10 credits, semester 1)
• Object-Oriented Programming with Applications (10 credits, semester 1)
• Risk-Neutral Asset Pricing (10 credits, semester 2)
• Stochastic Control and Dynamic Asset allocation (10 credits, semester 2)
• Monte Carlo Methods (5 credits, semester 2)
• Research-Linked Topics (10 credits, semesters 1 and 2)

Optional courses - Computational stream:

• Numerical Methods for Stochastic Differential Equations [compulsory] (5 credits, semester 2)
• Numerical Partial Differential Equations [compulsory] (10 credits, semester 2)
• Programming Skills - HPC MSc (10 credits, semester 1)
• Parallel Numerical Algorithms - HPC MSc (10 credits, semester 1)

Optional courses - Financial stream:

• Financial Risk Theory [compulsory] (10 credits, semester 2)
• Optimization Methods in Finance [compulsory] (10 credits, semester 2)
• Advanced Time Series Econometrics (10 credits, semester 2)
• Credit Scoring (10 credits, semester 2)
• Computing for Operational Research and Finance (10 credits, semester 1)
• Financial Risk Management (10 credits, semester 2)
• Stochastic Optimization (5 credits, semester 2)

Learning outcomes

At the end of this programme you will have:

• developed personal communications skills, initiative, and professionalism within a mathematical context
• developed transferable skills that maximise your prospects for future employment, including writing, oral presentation, team-working, numerical and logical problem-solving, planning and time-management
• improved your ability to convey ideas in an articulate fashion, to build upon previous mathematical training and further develop logic and deductive skills
• mastered standard and advanced mathematical tools used to solve applied problems relevant to the mathematical finance industry
• developed quantitative and computational skills for the proficient fulfilment of tasks in the financial sector

Career opportunities

Graduates can expect to go on to work in major financial institutions or to continue their studies by joining PhD programmes.

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This degree, offered jointly by the Department of Computer Science and the Department of Economics, allows you to specialise in modern quantitative finance and computational methods for financial modelling. This is an attractive advanced qualification, especially suitable if you are seeking employment in asset structuring, product pricing or risk management, among other fields.

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

Why choose this course?

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

- You will learn modern quantitative finance and computational methods for financial modeling. 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.

- 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 departments have expertise in a wide set of areas in Computer Science and in Economics, and the topics taught reflect these areas of excellence.

- Computer Science hosts one of strongest research groups in Machine Learning (the science of systems that learn from data).

- In the most recent Research Assessment Exercise (RAE 2008), Computer Science ranked 11th and Economics ranked 8th for their research output.

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

- Computer Science 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:
Throughout your degree, you will have the opportunity to acquire the following skills:

- Knowledge of the working of financial markets and their role in the context of global economy.
- Knowledge of modern mathematical and computational techniques used in finance.
- Knowledge of key ideas, principles, and methods of machine learning and their applications in finance.
- Ability to apply methods of computational finance to practical problems in computational finance, including pricing of derivatives and risk assessment.
- Ability to analyse and critically evaluate methods and general principles of computational finance and their applicability to specific problems; to critically evaluate validity and practicality of results.
- Ability to analyse and critically evaluate applicability of machine learning algorithms to problems in finance.
- Ability to implement methods of computational finance and machine learning using object-oriented programming languages and
modern data management systems.
- 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 highly employable and, in recent years, have entered many different finance-related areas, including careers as financial analysts, accountants, bankers, journalists and business analysts. Our graduates are currently working for firms such as Accenture, TNS, RBS, Deloitte, and Baker and McKenzie. 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 both departments. They offer application and interview coaching, career strategy discussions, and the opportunity to network with major finance employers on campus. Our careers service is provided by the Careers Group, the main provider of graduate recruitment services in London. Thus, you will have additional access to a wealth of presentations and networking opportunities which make the most of London’s financial centre.

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 .

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This degree, offered jointly by the Department of Computer Science and the Department of Economics, allows you to specialise in modern quantitative finance and computational methods for financial modelling. This is an attractive advanced qualification, especially suitable if you are seeking employment in asset structuring, product pricing or risk management, among other fields.

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/msccomputationalfinance(yearinindustry).aspx

Why choose this course?

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

- You will learn modern quantitative finance and computational methods for financial modeling. 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.

- 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 departments have expertise in a wide set of areas in Computer Science and in Economics, and the topics taught reflect these areas of excellence.

- Computer Science hosts one of strongest research groups in Machine Learning (the science of systems that learn from data).

- In the most recent Research Assessment Exercise (RAE 2008), Computer Science ranked 11th and Economics ranked 8th for their research output.

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

- Computer Science 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:

- Knowledge of the working of financial markets and their role in the context of global economy.
- Knowledge of modern mathematical and computational techniques used in finance.
- Knowledge of key ideas, principles, and methods of machine learning and their applications in finance.
- Ability to apply methods of computational finance to practical problems in computational finance, including pricing of derivatives and risk assessment.
- Ability to analyse and critically evaluate methods and general principles of computational finance and their applicability to specific problems; to critically evaluate validity and practicality of results.
- Ability to analyse and critically evaluate applicability of machine learning algorithms to problems in finance.
- Ability to implement methods of computational finance and machine learning using object-oriented programming languages and modern data management systems.
- 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 highly employable and, in recent years, have entered many different finance-related areas, including careers as financial analysts, accountants, bankers, journalists and business analysts. Our graduates are currently working for firms such as Accenture, TNS, RBS, Deloitte, and Baker and McKenzie. 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 both departments. They offer application and interview coaching, career strategy discussions, and the opportunity to network with major finance employers on campus. Our careers service is provided by the Careers Group, the main provider of graduate recruitment services in London. Thus, you will have additional access to a wealth of presentations and networking opportunities which make the most of London’s financial centre.

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 .

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Doctorate study in Computational Physics is an opportunity to engage in rigorous scholarly pursuit, and to contribute original research to a body of academia.

At the School of Mathematics and Physics, you will have the opportunity to advance your knowledge of computational physics, while developing your research skills and working with specialists. Computational Physics is a fundamental area of study that underpins a vast array of topics. During your research, you may have the opportunity to develop national and international collaborations.

Research in Computational Physics covers a broad spectrum, including the distinct areas of nanostructured soft matter, active matter, materials science and molecular biophysics. You benefit from dedicated academic supervisors, in-depth training programmes and specialist computational facilities.

Research Areas, Projects & Topics

Main Research Areas:
-Nanostructured Soft Matter
-Active Matter
-Materials Science
-Molecular Biophysics

For detailed information about the School’s research activity please visit: http://www.lincoln.ac.uk/home/smp/research/

How You Study

You can benefit from specialist computational facilities, training programmes to enhance your research skills and support from dedicated academic supervisors. You will be supported and encouraged to submit papers to international scientific journals, present your findings at conferences and share knowledge with colleagues across the University.

Due to the nature of postgraduate research programmes, the vast majority of your time will be spent in independent study and research. You will have meetings with your academic supervisor, however the regularity of these will vary depending on your own individual requirements, subject area, staff availability and the stage of your programme.

How You Are Assessed

A PhD is usually awarded based on the quality of your thesis and your ability in an oral examination (viva voce) to present and successfully defend your chosen research topic.

Career and Personal Development

This research programme is designed to allow you to expand your knowledge and expertise in an area of specific interest. It provides the opportunity to develop an in-depth foundation for further research or progression to careers across the broad spectrum of computational physics-related industries and in academia.

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Our MSc Computational Finance equips you with the core concepts and mathematical principles of modern quantitative finance, plus the operational skills to use computational packages (mainly Matlab) for financial modelling.

We provide practical, hands-on learning about how modern, highly computerised financial markets work, how assets should be priced, and how investors should construct a portfolio of assets. In addition to traditional topics in derivatives and asset pricing, we place a special emphasis on risk management in non-Gaussian environment with extreme events.

You master these areas through studying topics including:

- Non-linear and evolutionary computational methods for derivatives pricing and portfolio management
- Applications of calculus and statistical methods
- Computational intelligence in finance and economics
- Financial markets

You also graduate with an understanding of the use of artificial financial market environments for stress testing, and the design of auctions and other financial contracts.

Our Centre for Computational Finance and Economic Agents is an innovative and laboratory-based teaching and research centre, with an international reputation for leading-edge, interdisciplinary work combining economic and financial modelling with computational implementation.

Our research is geared towards real-world, practical applications, and many of our academic staff have experience of applying their findings in industry and in advising the UK government.

Read less

Our highly sought-after graduates benefit from a programme that integrates training in identifying, framing and effectively researching social problems with a leading computational approach to social science.

Furthermore, we are home to the Centre for Research in Social Simulation (CRESS) and its world-leading expertise in agent-based modelling.

PROGRAMME OVERVIEW

Interest in simulation has grown rapidly in the social sciences. New methods have been developed to tackle this complexity. This programme will integrate traditional and new methods, to model complexity, evolution and the adaptation of social systems.

These new methods are having an increasing influence on policy research through a growing recognition that many social problems are insufficiently served by traditional policy modelling approaches.

The Masters in Social Science and Complexity will equip you to develop expertise in the methods necessary to tackle complex, policy-relevant, real-world social problems through a combination of traditional and computational social science methods, and with a particular focus on policy relevance.

PROGRAMME STRUCTURE

This programme is studied full-time over one academic year and part-time over two academic years. It consists of eight taught modules and a dissertation. The following modules are indicative, reflecting the information available at the time of publication. Please note that not all modules described are compulsory and may be subject to teaching availability and/or student demand.
-Data Analysis
-Field Methods
-Computational Modelling
-Theory Model Data
-Modelling the Complex World
-Policy Modelling
-Theory and Method
-Statistical Modelling
-Evaluation Research
-Dissertation

EDUCATIONAL AIMS OF THE PROGRAMME

The main aims of the programme are to:
-Provide an appropriate training for students preparing MPhil/PhD theses, or for 
 students going on to employment involving the use of social science and policy research
-Provide training that fully integrates social science, policy modelling and computational methodologies to a high standard
-Provide training resulting in students with high quality analytic, methodological, computational and communication skills

PROGRAMME LEARNING OUTCOMES
The programme provides opportunities for students to develop and demonstrate knowledge and understanding, skills, qualities and other attributes in the following areas:
-Develop skills in tackling real world policy problems with creativity and sound methodological judgment
-Cover the principles of research design and strategy, including formulating research 
questions or hypotheses and translating these into practicable research designs and models
-Introduce students to the methodological and epistemological issues surrounding research in the social sciences in general and computational modelling in particular
-Develop skills in programming in NetLogo for the implementation of agent-based models for the modelling of social phenomena
-Develop skills in the acquisition and analysis of social science data
-Make students aware of the range of secondary data available and equip them to evaluate its utility for their research
-Develop skills in searching for and retrieving information, using library and Internet resources
-Develop skills in the use of SPSS, and in the main statistical techniques of data analysis, including multivariate analysis
-Develop skills in the use of CAQDAS software for the analysis of qualitative data
-Develop skills in writing, in the preparation of a research proposal, in the presentation ofresearch results and in verbal communication
-Help students to prepare their research results for wider dissemination, in the form of seminar papers, conference presentations, reports and publications, in a form suitable for a range of audiences, including academics, stakeholders, policy makers, professionals, service users and the general public

Knowledge and understanding
-Show advanced knowledge of qualitative, quantitative and computational methodologies in the social science
-Show advanced knowledge of modelling methodologies, model construction and analysis
-Show critical understanding of methodological and epistemological challenges of social science and computer modelling
-Show critical awareness and understanding of the methodological implications of a range of sociological theories and approaches
-Show understanding the use and value of a wide range of different research approaches across the quantitative and qualitative spectra
-Show advanced knowledge in data collection, analysis and data driven modelling
-Show advanced knowledge of policy relevant social science research and modelling
-Show advanced understanding of the policy process and the role of social science and modelling therein
-Show advanced knowledge of statistical modelling

Intellectual / cognitive skills
-Systematically formulate researchable problems; analyse and conceptualise issues; critically appreciate alternative approaches to research; report to a range of audiences
-Conceptual development of Social Science and Complexity models to creatively enhance the understanding of social phenomena
-Integration of qualitative, quantitative and computational data
-Judgement of problem-methodology match
-Analyse qualitative and quantitative data drawn both from ‘real world’ and ‘virtual world’ environments, using basic and more advanced techniques, and draw warranted conclusions
-Develop original insights, questions, analyses and interpretations in respect of research questions
-Critically evaluate the range of approaches to research

Professional practical skills
-Formulate, design, plan, carry out and report on a complete research project
-Use the range of traditional and computational techniques employed in sociological research
-Ability to produce well founded, data driven and validated computational models
-Generate both quantitative and qualitative data through an array of techniques, and select techniques of data generation on appropriate methodological bases
-Employ a quantitative (SPSS) and qualitative software package to manage and analyse data
-Plan, manage and execute research as part of a team and as a sole researcher
-Ability to communicate research findings models in social science and policy relevant ways
-Ability to manage independent research

Key / transferable skills
-Communicate complex ideas, principles and theories by oral, written and visual means
-Apply computational modelling methodology to complex social issues in appropriate ways
-Creativity in approaching complex problems and a the ability of communicating and justifying problem solutions
-Apply computing skills for computational modelling, research instrument design, data analysis, and report writing and presentation
-Work to deadlines and within work schedules
-Work independently or as part of a team
-Demonstrate experience of a work environment

PLACEMENTS

On the MSc Social Science and Complexity, we offer the opportunity to take a research placement during the Easter vacation. This will provide you with first-hand experience of real-life policy research in action.

Organisations in which placements might be possible are a number of consultancies (e.g. Sandtable), government departments (e.g. Defra) and academic research centres (e.g. Centre for Policy Modelling at Manchester).

CAREER OPPORTUNITIES

Computational methods and especially computer-based simulations, are becoming increasingly important in academic social science and policy making.

Graduates might find career opportunities in government departments, consultancies, government departments, consultancies, NGOs and academia.

GLOBAL OPPORTUNITIES

We often give our students the opportunity to acquire international experience during their degrees by taking advantage of our exchange agreements with overseas universities.

In addition to the hugely enjoyable and satisfying experience, time spent abroad adds a distinctive element to your CV.

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About the course

Cognitive neuroscience relates cognitive and behavioural functions to the underlying brain systems. Computational neuroscience uses data to construct rigorous computational models of brain function. Put them together and these new disciplines are the key to explaining the relationship between brain and behaviour.

You’ll develop a broad and critical understanding of these two fields, along with an appreciation of different approaches to understanding brain function. Your range of computational and analytical skills, and an ability to generate and test hypotheses, will give you an excellent foundation for further research.

The course takes students from both life sciences and the physical sciences and engineering. Appropriate training is given to ensure all students can master the required skills and complete the course successfully.

Where your masters can take you

You’ll develop the skills and knowledge for all sorts of careers. Many of our graduates continue to PhD level. Others work as research associates and assistant psychologists for employers such as universities and the NHS. Throughout your course, you’ll have frequent reviews with your tutor to discuss your learning needs and objectives.

Applying psychology in the real world

Our ongoing collaborative projects with hospitals, mental health care units, the police and prison service, and several leading firms in business and industry will show you how psychology can be applied in the real world.

You’ll also benefit from our research excellence. We don’t just focus on one or two specialisms – with active researchers in most areas of psychology, we are consistently one of the highest-ranked research departments in the UK.

Our facilities

Whatever your particular interest, we have the facilities for your research. Our research environment was rated amongst the best in the country in the last national assessment. We are exceptionally well resourced for research in Social and Health Psychology, Clinical Psychology and Developmental Psychology, with a dedicated suite of rooms for different participant groups.

To give you the right tools for your research, there is a fully equipped neuroscience unit with excellent facilities for brain imaging, neuroanatomy, electrophysiology, behavioural neuroscience and computational neuroscience. We have access to a small-bore MRI device and to the University’s MRI facility for human studies.

Studentships and bursaries

Please contact us for the latest funding opportunities.

Core modules

Fundamentals of Cognitive Neuroscience; Fundamentals of Neuroscience; Computational Neuroscience 1: biologically grounded models; Mathematical Modelling and Research Skills; Computational Neuroscience 2: theoretical models; Brain Imaging and its Physical Foundations; Current Issues in Systems Neuroscience;Current Issues in Cognitive Neuroscience.

Teaching

Teaching is through lectures, seminars and laboratory classes.

Assessment

Examinations at the end of semesters one and two, written coursework and an extensive empirical research project over the summer.

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This degree develops your arts practice through the expressive world of creative computation. It provides you with the historical foundations, frameworks and critical skills to produce a series of projects for public exhibition. It is delivered by Computing with contributions from the Centre for Cultural Studies- http://www.gold.ac.uk/pg/ma-computational-arts/

What is computational art?

Computation consists of all the changes brought about by digital technology. Art is an open set of ways of acting inventively in culture. Mixing the two together in a systematic way gives us computational art. This is a very open field, and one that is set to expand enormously in the coming years. It is where the most exciting developments in technology and in culture can already be found. This degree will place you in the middle of this fast-evolving context.

Follow the links in the student profiles section for work produced by our graduates.

What will I learn?

This degree develops your arts practice through the expressive world of creative computation. Over a year (full-time) or two years (part-time) you will develop your artistic work and thinking through the challenge of developing a series of projects for public exhibition which will explore the technological and cultural ramifications of computation.

You will learn the fundamentals of programming and how to apply this knowledge expressively. You will work with popular open source programming environments such as Processing, OpenFrameworks, P5.js and Arduino, and will learn how to program in languages such as Java, Javascript and C++.

Since computational artworks don’t necessarily involve computers and screens, we also encourage students to produce works across a diverse range of media. Supported by studio technicians in state-of-the-art facilities, our students are producing works using tools such as 3D printers, laser cutters, robotics, wearable technologies, paint, sculpture and textiles.

You will also study contextual modules on computational art and the socio-political effects of technology. Modules in the Centre for Cultural Studies provide students with the historical foundations, frameworks, critical skills and confidence to express their ideas effectively. You will have the opportunity to learn the cultural histories of technology, to reflect on computation in terms of its wider cultural effects, and to understand the way in which art provides rigorous ways of thinking.

Through our masterclass series, we regularly invite world-class artists and curators to explain their work and engage in critical dialogue with the students. This allows you to develop a wider understanding of the contemporary art scene and how your work sits within the professional art world.

Contact the department

If you have specific questions about the degree, contact Theo Papatheodorou.

Modules

Programming for Artists 1- 15 credits
Programming for Artists 2- 15 credits
Workshops in Creative Coding 1- 15 credits
Final Project in Computational Arts- 60 credits
Physical Computing- N/A

Funding

Please visit http://www.gold.ac.uk/pg/fees-funding/ for details.

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This MSc teaches advanced analytical and computational skills for success in a data rich world. Designed to be both mathematically rigorous and relevant, the programme covers fundamental aspects of machine learning and statistics, with potential options in information retrieval, bioinformatics, quantitative finance, artificial intelligence and machine vision.

Degree information

The programme aims to provide graduates with the foundational principles and the practical experience needed by employers in the area of machine learning and statistics. Graduates of this programme will have had the opportunity to develop their skills by tackling problems related to industrial needs or to leading-edge research.

Students undertake modules to the value of 180 credits.

The programme consists of four core modules (60 credits), four optional modules (60 credits) and a research project (60 credits). Please note that not all combinations of optional modules will be available due to timetabling restrictions.

Core modules
-Supervised Learning
-Statistical Modelling and Data Analysis
-Graphical Models or Probabilistic and Unsupervised Learning
Plus one of:
-Applied Bayesian Methods
-Statistical Design of Investigations
-Statistical Computing
-Statistical Inference

Optional modules - students select 60 credits from the following list:
-Advanced Topics in Machine Learning
-Affective Computing and Human-Robot Interaction
-Applied Bayesian Methods
-Approximate Inference and Learning in Probabilistic Models
-Computational Modelling for Biomedical Imaging
-Information Retrieval and Data Mining
-Machine Vision
-Selected Topics in Statistics
-Optimisation
-Statistical Design of Investigations
-Statistical Inference
-Statistical Natural Language Programming
-Stochastic Methods in Finance
-Stochastic Methods in Finance 2
-Advanced Topics in Statistics
-Mathematical Programming and Research Methods
-Intelligent Systems in Business

Dissertation/report
All MSc students undertake an independent research project, which culminates in a dissertation of 10,000-12,000 words.

Teaching and learning
The programme is delivered through a combination of lectures, discussions, practical sessions and project work. Student performance is assessed through unseen written examinations, coursework, practical application and the project assessment process.

Careers

There is a strong national and international demand for graduates with skills at the interface of traditional statistics and machine learning. Substantial sectors of UK industry, including leading, large companies already make extensive use of computational statistics and machine learning techniques in the course of their business activities. Globally there are a large number of very successful users of this technology, many located in the UK. Areas in which expertise in statistics and machine learning is in particular demand include; finance, banking, insurance, retail, e-commerce, pharmaceuticals, and computer security. Graduates have gone on to further study at, for example, the Universities of Cambridge, Helsinki, Chicago, as well as at UCL. The MSc is also ideal preparation for a PhD, in statistics, machine learning or a related area.

Top career destinations for this degree:
-Statistical and Algorithm Analyst, Telemetry
-Decision Scientist, Everline
-Computer Vision Researcher, Slyce
-Data Scientist, YouGov
-Research Engineer, DeepMind

Employability
Scientific experiments and companies now routinely generate vast databases and machine learning and statistical methodologies are core to their analysis. There is a considerable shortfall in the number of qualified graduates in this area internationally. CSML graduates have been in high demand for PhD positions across the sciences. In London there are many companies looking to understand their customers better who have hired our CSML graduates. Similarly graduates now work in companies in, amongst others, Germany, Iceland, France and the US in large-scale data analysis. The finance sector has also hired several graduates recently.

Why study this degree at UCL?

The Centre for Computational Statistics and Machine Learning (CSML) is a major European Centre for machine learning having coordinated the PASCAL European Network of Excellence.

Coupled with the internationally renowned Gatsby Computational Neuroscience and Machine Learning Unit, and UCL Statistical Science, this MSc programme draws on world-class research and teaching talents. The centre has excellent links with world-leading companies in internet technology, finance and related information areas.

The programme is designed to train students in both the practical and theoretical sides of machine learning. A significant grounding in computational statistics is also provided.

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This course provides specialist skills in core systems biology with a focus on the development of computational and mathematical research skills. It specialises in computational design, providing essential computing and engineering skills that allow you to develop software to program biological systems.

This interdisciplinary course is based in the School of Computing Science and taught jointly with the Faculty of Medical Sciences and the School of Mathematics and Statistics. The course is ideal for students aiming for careers in industry or academia. We cater for students with a range of backgrounds, including Life Sciences, Computing Science, Mathematics and Engineering.

Computational Systems Biology is focused on the study of organisms from a holistic perspective. Computational design of biological systems is essential for allowing the construction of complex and large biological systems.

We provide a unique, multidisciplinary experience essential for understanding systems biology. The course draws together the highly-rated teaching and research expertise of our Schools of Computing Science, Mathematics and Statistics, Biology, and Cell and Molecular Biosciences. The course also has strong links with Newcastle's Centre for Integrated Systems Biology of Ageing and Nutrition (CISBAN).

Our course is designed for students from both biological and computational backgrounds. Prior experience with computers or computer programming is not required. Students with mathematical, engineering or other scientific backgrounds are also welcome to apply.

The course is part of a suite of related programmes that also include:
-Bioinformatics MSc
-Synthetic Biology MSc
-Computational Neuroscience and Neuroinformatics MSc

All four programmes share core modules, creating a tight-knit cohort. This encourages collaborations on projects undertaking interdisciplinary research.

Project work

Your five month research project gives you a real opportunity to develop your knowledge and skills in depth in Systems Biology. You have the opportunity to work closely with a leading research team in the School and there are opportunities to work on industry lead projects. You will have one-to-one supervision from an experienced member of the faculty, supported with supervision from associated senior researchers and industry partners as required.

The project can be carried out:
-With a research group at Newcastle University
-With an industrial sponsor
-With a research institute
-At your place of work

Placements

Students have a unique opportunity to complete a work placement with one of our industrial partners as part of their projects.

Previous students have found placements with organisations including:
-NHS Business Services Authority
-Waterstons
-Metropolitan Police
-Accenture
-IBM
-Network Rail
-Nissan
-GSK

Accreditation

We have a policy of seeking British Computer Society (BCS) accreditation for all of our degrees, so you can be assured that you will graduate with a degree that meets the standards set out by the IT industry. Studying a BCS-accredited degree provides the foundation for professional membership of the BCS on graduation and is the first step to becoming a chartered IT professional.

The School of Computing Science at Newcastle University is an accredited and a recognised Partner in the Network of Teaching Excellence in Computer Science.

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