Masters Degrees (Mathematical Modelling)

This programme develops mathematical modelling skills and provides mathematical techniques required by industry.

The period October to June is devoted to lectures, tutorials and practical sessions comprising the core modules.

This is followed by a period of about 14 weeks devoted to an individual project either in an industrial or engineering company or at the University.

Core study areas include mathematical modelling, regular and chaotic dynamics, programming and numerical methods, advanced reliability, availability and maintainability, elements of partial differential equations, static and dynamic optimisation and fluid mechanics.

See the website http://www.lboro.ac.uk/study/postgraduate/programmes/departments/mathematics/industrial-maths-modelling/

Programme modules

Compulsory Modules:
Semester 1
- Mathematical Modelling I
- Regular and Chaotic Dynamics
- Programming and Numerical Methods
- Advanced Reliability, Availability and Maintainability

Semester 2
- Mathematical Modelling II
- Elements of Partial Differential Equations
- Static and Dynamic Optimisation
- Fluid Mechanics

Assessment

A combination of written examinations, reports, individual and group projects, and verbal presentations.

Careers and further Study

Graduate employment over a wide range of industries encompassing aerospace, automotive electronics, and computer interests as well as software houses, insurance companies, and research establishments and institutions.

Scholarships and sponsorships

A limited number of scholarships are available for this programme as well as the loyalty bonus scheme which reduces fees for Loughborough graduates.

Why choose mathematics at Loughborough?

Mathematics at Loughborough has a long history of innovation in teaching, and we have a firm research base with strengths in both pure and applied mathematics as well as mathematics education.

The Department comprises more than 34 academic staff, whose work is complemented and underpinned by senior visiting academics, research associates and a large support team.

The programmes on offer reflect our acknowledged strengths in pure and applied research in mathematics, and in some cases represent established collaborative training ventures with industrial partners.

- Mathematics Education Centre (MEC)
The Mathematics Education Centre (MEC) at Loughborough University is an internationally renowned centre of research, teaching, learning and support. It is a key player in many high-profile national initiatives.
With a growing number of academic staff and research students, the MEC provides a vibrant, supportive community with a wealth of experience upon which to draw.
We encourage inquiries from students who are interested in engaging in research into aspects of learning and teaching mathematics at Masters, PhD and Post Doc levels. Career prospects With 100% of our graduates in employment and/or further study six months after graduating, career prospects are excellent. Graduates go on to work with companies such as BAE Systems, Citigroup, Experian, GE Aviation, Mercedes Benz, Nuclear Labs USA and PwC.

- Career prospects
With 100% of our graduates in employment and/or further study six months after graduating, career prospects are excellent. Graduates
go on to work with companies such as BAE Systems, Citigroup, Experian, GE Aviation, Mercedes Benz, Nuclear Labs USA and PwC.

Find out how to apply here http://www.lboro.ac.uk/study/postgraduate/programmes/departments/mathematics/industrial-maths-modelling/

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Most things in the real world are complex and difficult to understand, from biological systems to the financial markets to industrial processes, but explaining them is essential to making progress in the modern world. Mathematical modelling is a fundamental tool in the challenge to understand many of these systems, and is an essential part of contemporary applied mathematics. By developing, analysing and interpreting mathematical and computational models we gain insight into these complex processes, as well as giving a framework in which to interpret experimental data.

To fully capitalise on these tools, there is a fundamental need in both academic research and industry for a new generation of scientists trained to work at the interdisciplinary frontiers of mathematics and computation. These scientists require the ability to assimilate and understand information from other disciplines, communicate with and enthuse other researchers, as well as having the advanced mathematical and computational skills needed.

MSc Mathematical Modelling is a one year master’s level course at the interfaces of Mathematics, Computer Science, Systems Biology and Chemical Engineering. Interdisciplinary mathematical modelling in the School of Mathematics at the University of Birmingham takes place in a thriving outward-facing community with specialities including mathematical biology, fluid mechanics, mathematical finance and industrial modelling. The School collaborates widely with multiple disciplines, including Biological and Medical Sciences, Chemical Engineering and within industry. In particular, Birmingham is an emerging centre for multidisciplinary Biological Systems Science research, and is in a unique position, being adjacent to one of the largest super-hospitals in Europe, catering for a highly diverse population.

The programme is specifically tailored to develop students from a strong mathematics background into becoming genuinely multidisciplinary scientists. You will have the opportunity to develop your mathematical and computational modelling skills, whilst at the same time being trained in cutting-edge interdisciplinary techniques, including the option of practical work. You will learn how to diversify your skills into other fields, and how to work with research leaders and other students from different disciplines.

About the School of Mathematics

The School of Mathematics is one of seven schools in the College of Engineering and Physical Sciences. The school is situated in the Watson Building on the main Edgbaston campus of the University of Birmingham. There are about 50 academic staff, 15 research staff, 10 support staff, 60 postgraduate students and 600 undergraduate students.
At the School of Mathematics we take the personal development and careers planning of our students very seriously. Jointly with the University of Birmingham's Careers Network we have developed a structured programme to support maths students with their career planning from when they arrive to when they graduate and beyond.

Funding and Scholarships

There are many ways to finance your postgraduate study at the University of Birmingham. To see what funding and scholarships are available, please visit: http://www.birmingham.ac.uk/postgraduate/funding

Open Days

Explore postgraduate study at Birmingham at our on-campus open days.
Register to attend at: http://www.birmingham.ac.uk/postgraduate/visit

Virtual Open Days

If you can’t make it to one of our on-campus open days, our virtual open days run regularly throughout the year. For more information, please visit: http://www.pg.bham.ac.uk

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This MSc provides an ideal foundation for students wishing to advance their mathematical modelling skills. The programme teaches students the basic concepts which arise in a broad range of technical and scientific problems and illustrates how these may be applied in a research context to provide powerful solutions.

Degree information

Students develop an understanding of the processes undertaken to arrive at a suitable mathematical model and are taught the fundamental analytical techniques and computational methods used to develop insight into system behaviour. The programme introduces a range of problems - industrial, biological and environmental - and associated conceptual models and solutions.

Students undertake modules to the value of 180 credits.

The programme consists of five core modules (75 credits), three optional modules (45 credits), and a research dissertation (60 credits). The part-time option normally spans two years. The eight taught modules are spread over the two years. The research dissertation is taken in the summer of the second year.

Core modules
-Advanced Modelling Mathematical Techniques
-Nonlinear Systems
-Operational Research
-Computational and Simulation Methods
-Frontiers in Mathematical Modelling and its Applications

Optional modules
-Asymptotic Methods & Boundary Layer Theory
-Biomathematics
-Cosmology
-Evolutionary Game Theory and Population Genetics
-Financial Mathematics
-Geophysical Fluid Dynamics
-Mathematical Ecology
-Quantitative and Computational Finance
-Real Fluids
-Traffic Flow
-Waves and Wave Scattering

Dissertation/report
All MSc students undertake an independent research project, which culminates in a dissertation of approximately 15,000-words and a project presentation.

Teaching and learning
The programme is delivered through seminar-style lectures and problem and computer-based classes. Student performance is assessed through a combination of unseen examination and coursework. For the majority of courses, the examination makes up between 90–100% of the assessment. The project is assessed through the dissertation and an oral presentation.

Careers

Our graduates have found employment in a wide variety of organisations such as Hillier-Parker, IBM, Swissbank, Commerzbank Global Equities, British Gas, Harrow Public School, Building Research Establishment and the European Centre for Medium-Range Weather-Forecasting. First destinations of recent graduates include:
-R.T.E: Engineer
-Tower Perrins: Actuarist
-Deloitte: Quantitative Analyst
-UCL: Research Associate
-C-View: Quantitative Trader
-One-to-One: Maths Tutor
-UCL Research Degree - Mathematics
-Duff & Phelps Ltd: Financial Engineer
-Bank of Tokyo Mitsubishi: Assistant Compliance Officer

Employability
The finance, actuarial and accountancy professionals are constantly in demand for high-level mathematical skills and recent graduates have taken positions in leading finance-related companies such as UBS, Royal Bank of Scotland, Societe Generale, PricewaterhouseCoopers, Deloitte, and KPMG.

In the engineering sector, recent graduates from the MSc include a mathematical modeller at Steet Davies Gleave, a leading Transportation Planning Consultancy; and a graduate trainee at WesternGreco, a business segment of Schlumberger that provides reservoir imaging, monitoring, and development services. In addition, a number of graduates have remained in education either progressing to a PhD or entering the teaching profession.

Why study this degree at UCL?

UCL Mathematics is internationally renowned for its excellent individual and group research that involves applying modelling techniques to problems in industrial, biological and environmental areas.

The department hosts a stream of distinguished international visitors. In recent years four staff members have been elected fellows of the Royal Society, and the department publishes the highly regarded research journal Mathematika.

This MSc enables students to consolidate their mathematical knowledge and formulate basic concepts of modelling before moving on to case studies in which models have been developed for issues motivated by industrial, biological or environmental considerations.

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The primary aim of this course is to educate you to MSc level in the theoretical and practical aspects of mathematical problem solving, mathematical model development, creating software solutions and communication of results.

This course provides training in the use and development of reliable numerical methods and corresponding software. It aims to train graduates with a mathematical background to develop and apply their skills to the solution of real problems. It covers the underlying mathematical ideas and techniques and the use and design of mathematical software. Several application areas are examined in detail. It develops skills in mathematical problem-solving, scientific computing, and technical communication.

Training is also provided in general computing skills, mathematical typsetting, mathematical writing, desktop and web-based mathematical software development, and the use of computer languages and packages including Mathematica, parallel computing, C#, 3D graphics and animation, and visualisation.

The MSC is now available fully online and can be taken over 12 months full time or 24 months part time.

Visit the website: http://www.ucc.ie/en/ckr36/

Course Details

By the end of the course, you will be able to:

- use the description of a real world problem to develop a reasonable mathematical model in consultation with the scientific literature and possibly experts in the area
- carry out appropriate mathematical analysis
- select or develop an appropriate numerical method and write a computer programme which gives access to a sensible solution to the problem
- present and interpret these results for a potential client or a non-technical audience

Modules

Module descriptions - http://www.ucc.ie/calendar/postgraduate/Masters/science/page05.html#mathematical

AM6001 Introduction to Mathematica (5 credits)
AM6002 Numerical Analysis with Mathematica (5 credits)
AM6003 Cellular Automata (5 credits)
AM6004 Applied Nonlinear Analysis (Computational Aspects) (5 credits)
AM6005 Modelling of Systems with Strong Nonlinearities (5 credits)
AM6006 Mathematical Modelling of Biological Systems with Differential Equations (5 credits)
AM6007 Object Oriented Programming with Numerical Examples (10 credits)
AM6008 Developing Windowed Applications and Web-based Development for Scientific Applications (5 credits)
AM6009 3D Computer Graphics and Animation for Scientific Visualisation (5 credits)
AM6010 Topics in Applied Mathematical Modelling (5 credits)
AM6011 Advanced Mathematical Models and Parallel Computing with Mathematica (5 credits)
AM6012 Minor Dissertation (30 credits)

Format

The course places great emphasis on hands-on practical skills. There is a computer laboratory allocated solely for the use of MSc students. PCs are preloaded with all the required software and tools. Online students are expected to have a suitable PC or laptop available; all required software is provided for installation to faciliate course work. Online teaching hours, involving lecturers, tutorials and practical demonstrations, usually take place in the morninbg. The rest of the time, you are expected to do exercises, assignments and generally put in the time required to acquire key skills.

Assessment

Continuous assessment is the primary method of examining. In each module, typically 40% of the marks are available for take-home assignments and the remaining 60% of marks are examined by a practical computer-based examination. Final projects are read and examined by at least two members of staff.

For more information, please see the Book of Modules 2015/2016 - http://www.ucc.ie/calendar/postgraduate/Masters/science/page05.html#mathematical

Careers

Quantitative graduates with software skills are in high demand in industry according to the Governments Expert Group on Future Skills Needs. Demand for these skills is project to rise over the coming years not just in Ireland but in the EU and globally. Graduates have recently secured jobs in the following areas: banking, financial trading, consultancy, online gambling firms, software development, logistics, data analysis and with companies such as AIB, McAfee, Fexco, DeCare Systems, MpStor, the Tyndall Institute, Matchbook.com, First Derivatives and KPMG.

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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Overview

If you’re a highly numerate graduate with a desire to apply your quantitate skills to the real world, or a practitioner working in the sector, this course will take you to the next level and prepare you for a career as a transport modelling specialist.

Experience a course designed in collaboration with employers, learning skills the industry desperately needs to unlock the full potential of big data.

Learn to think creatively, beyond the standard application of established solutions, and use your technical expertise across multiple scenarios.

Develop and apply mathematical models to represent, understand, analyse and optimise the performance of transportation networks and flows. In addition:
• Use mathematical models in different contexts of transport analysis
• Develop solutions and strategies based on mathematical models
• Apply optimisation algorithms to traffic network models
• Write computer programmes to solve problems and visualise model outputs
• Write scientific reports for technical and lay audiences
• Critically and creatively evaluate current issues
• Develop research and advanced scholarship skills.

And experience what it is like to be part of a project team working across numerous subject boundaries relevant to the transport sector. Through this, gain insights into how transport planning, social science, economics, environmental science, modelling and engineering can work together to design transport solutions to global challenges. This industry-inspired initiative will enable you to apply your knowledge to real world transport issues in the field.

Your colleagues will be among the best and brightest from the UK and across the globe. Together you will learn mathematical modelling skills that can be applied to design smarter transport solutions founded on robust methods.

You can also study this subject at Postgraduate Diploma level, part time or full time.

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About Computer Science

Computer Science is one of the drivers of technological progress in all economic and social spheres. Those graduating with an M.Sc. in Computer Science are specialists in at least one field of computer science who have wide-ranging science-based methodological expertise.
Graduates are able to define, autonomously and comprehensively, computer science problems and their applications, structure them and build abstract models. Moreover, they are able to define and implement solutions that are at the state of the art of technology and science.

Features

– A broad, international and relevant selection of courses
– As a student, you will work on cutting-edge research projects
– Individual guidance in small learning groups
– Excellent enterprise relations maintained by the chairs and institutes
– Numerous partnerships with universities throughout the world, including a double degree programme with the Institut national des sciences appliquées de Lyon (INSA)

Syllabus

The programme offers the following five focus modules:
1) Algorithms and Mathematical Modelling
2) Programming and Software Systems
3) Information and Communication Systems
4) Intelligent Technical Systems
5) IT Security and Reliability
1) Algorithms and Mathematical Modelling: This module teaches you about determinstic and stochastic algorithms, their implementation, evaluation and optimisation. You will acquire advanced knowledge of computer-based mathematical methods – particularly in the areas of algorithmic algebra and computational stochastics – as well as developing an in-depth expertise in mathematical modelling and complexity analysis of discrete and continuous problems.
2) Programming and Software Systems: This module imparts modern methods for constructing large-scale software systems, as well as creating and using software authoring, analysis and optimisation tools. In this module you will consolidate your knowledge of the various programming paradigms and languages, the structure of language processing systems, and learn to deal with parallelism in program procedures.
3) Information and Communication Systems: In this module you will study the interactions of the classic computer science areas of information systems and computer networks. This focus area represents an answer to the problem of increasing volume and complexity of worldwide information distribution and networks, and for the growing requirements on quality and performance of computer communication. Additionally, you will learn to transfer database results to multimedia data.
4) Intelligent Technical Systems: In this module you are acquainted with digital image and signal processing, embedded systems and applications of intelligent technical systems in industrial and assistance systems, which are necessary for production automation and process control, traffic control, medical and building technology. You will learn to develop complex applications using computer systems and deal with topics such as image reconstruction, camera calibration, sensor data fusion and optical measurement technology.
5) IT Security and Reliability: This module group is concerned with security and reliability of IT systems, e.g. in hardware circuitry and communication protocols, as well as complex, networked application systems. To ensure the secure operation of these systems you will learn design methodology, secure architectures and technical implementation of the underlying components.

Language requirements

Unless English is your native language or the language of your secondary or undergraduate education, you should provide an English language certificate at level B2 CEFR, e.g. TOEFL with a minimum score of 567 PBT, 87 iBT or ITP 543 (silver); IELTS starting from 5.5; or an equivalent language certificate.

To facilitate daily life in Germany, it would be beneficial for you to have German language skills at level A1 CEFR (beginner’s level). If you do not have any German skills when starting out on the programme, you will complete a compulsory beginner’s German course during your first year of study.

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Infectious diseases remain a major contributor to the global burden of disease, with HIV, malaria, measles, diarrhoeal disease and respiratory infections responsible for over 50% of premature deaths worldwide. However the availability of resources for interventions is limited in comparison with the scale of the challenges faced. Over the last decade there has been increasing recognition of the value of epidemiological analysis and mathematical modelling in aiding the design and interpretation of clinical trials from a population perspective and, downstream, to guide implementation, monitoring and evaluation of intervention effectiveness. The Epidemiology, Evolution and Control of Infectious Diseases (EECID) stream provides a research-based training in infectious disease epidemiology, mathematical modelling and statistics, genetics and evolution, and computational methods. The focus of the course is inter-disciplinary, with a strong applied public health element.

Based in the Department of Infectious Disease Epidemiology in the Faculty of Medicine, the stream provides an opportunity to learn, in a supportive and stimulating environment, from leaders in the field who are actively engaged in research and advise leading public health professionals, policy-makers, governments, international organisations and pharmaceutical companies, both nationally and internationally, on a range of diseases include pandemic influenza, HIV, TB, malaria, polio and neglected tropical diseases (NTDs).

This stream is linked to the Wellcome Trust 4-year PhD programme in the Epidemiology, Evolution and Control of Infectious Diseases which includes up to 5 funded studentships each year. Up to 3 further 1+3 MRC studentships are also available each year.

The emphasis of the course will be to provide a thorough training in epidemiology, mathematical modelling and statistics, and genetics and evolution, as applied to infectious diseases. This research-orientated training will incorporate taught material, practical sessions in statistical software (R) and C programming as well as wider generic training in the research and communication skills needed to interact with public health agencies. Through the two research-based projects students will be exposed to the latest developments in the field and will gain first-hand experience in applying the methods they are taught to questions of public-health relevance.

Individuals who complete the course will have developed the ability to:

-Describe the biology, epidemiology and control of major global infectious diseases
-Interpret and present epidemiological data
-Undertake statistical analysis of infectious disease data including applying modern methods for statistical inference
-Develop and apply mathematical models to understand infectious disease dynamics, evolution and control
-Analyse genetic data using modern techniques and interpret their relevance to infectious disease epidemiology
-Critically evaluate research papers and reports
-Write and defend research reports and publications
-Communicate effectively through writing, oral presentations and IT to facilitate further study or employment in epidemiology and public health
-Exercise a range of transferable skills

This will be achieved through a course of lectures, seminars, tutorials and technical workshops. Please note that Postgraduate Diplomas and Certificates for part-completion are not available for this course.

The stream will be based in the Department of Infectious Disease Epidemiology on the St Mary’s campus of Imperial College London.

Each student chooses two projects over the course of the year from the wide range available. Students are guided in this choice by the stream organiser and their personal tutor and are advised to take contrasting projects to ensure a balanced training.

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he contribution of mathematical and computational modelling to the understanding of biological systems has rapidly grown in recent years. This discipline encompasses a wide range of life science areas, including ecology (e.g. population dynamics), epidemiology (e.g. spread of diseases), medicine (e.g. modelling cancer growth and treatment) and developmental biology.

This programme aims to equip students with the necessary technical skills to develop, analyse and interpret models applied to biological systems. Course work is supported by an extended and supervised project in life science modelling.

Students will take a total of 8 courses, 4 in each of the 1st and 2nd Semesters followed by a 3-month Project in the summer. A typical distribution for this programme is as follows:

Core courses

Modelling and Tools;
Mathematical Ecology;
Dynamical Systems;
Mathematical Biology and Medicine.

Optional Courses

Optimization;
Numerical Analysis of ODEs;
Applied Mathematics;
Statistical Methods;
Stochastic Simulation;
Partial Differential Equations;
Numerical Analysis;
Geometry;
Climate Change: Causes and Impacts;
Biologically Inspired Computation;
Climate Change: Mitigation and Adaptation Measures.

Typical project subjects

Population Cycles of Forest Insects;
Modelling Invasive Tumour Growth;
The replacement of Red Squirrels by Grey Squirrels in the UK;
Wiring of Nervous System;
Vegetation Patterning in Semi-arid Environments;
Daisyworld: A Simple Land Surface Climate Model.

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Climate change is recognised as having potentially huge impacts on the environment and on human society. This programme aims to provide an understanding of climate change causes, impacts, mitigation and adaptation measures from a life science perspective in conjunction with developing a wide variety of mathematical modelling skills that can be used to investigate the impacts of climate change.

The programme closely follows the structure of our Applied Mathematical Sciences MSc. Two of the mandatory courses will specifically focus on understanding the issues related to climate change and are taught by the School of Life Sciences.

Students will take a total of 8 courses, 4 in each of the 1st and 2nd Semesters followed by a 3-month Project in the summer. A typical distribution for this programme is as follows:

Core courses

Modelling and Tools;
Mathematical Ecology;
Climate Change: Causes and Impacts;
Climate Change: Mitigation and Adaptation Measures;
Dynamical Systems (recommended);
Stochastic Simulation (recommended)

Optional Courses

Optimization;
Mathematical Biology and Medicine;
Numerical Analysis of ODEs;
Applied Mathematics;
Statistical Methods;
Applied Linear Algebra;
Partial Differential Equations;
Numerical Analysis;
Geometry;
Bayesian Inference.

Typical project subjects

Population Cycles of Forest Insects;
Climate Change Impact;
The replacement of Red Squirrels by Grey Squirrels in the UK;
Vegetation Patterns in Semi-arid Environments;
Daisyworld: A Simple Land Surface Climate Model.

The final part of the MSc is an extended project in mathematical modelling the impacts of climate change on environmental systems, giving the opportunity to investigate a topic in some depth guided by leading research academics.

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The MPhil in Strategy, Marketing and Operations (SMO) is an intensive nine-month programme that prepares you for continuation to the CJBS PhD in the fields of Strategy, Marketing or Operations Management.

Educational aims of the programme:

- To prepare students for doctoral work in the areas of operations management or marketing at Judge Business School (JBS) (the programme is an integral part of the JBS PhD programmes in these disciplines)

By:
- providing teaching in research methodology, in particular in econometrics and mathematical modelling
- providing teaching in foundational subjects, such as economics
- providing research seminars in which students will learn about the current debates in the field and about the canonical literature that underpins these debates
- providing guidance on the structuring, writing and critiquing of academic research papers
- giving students the opportunity to experience research first-hand, either through an individual research project or a dissertation.

See the website http://www.graduate.study.cam.ac.uk/courses/directory/bmjbmpmso

Format

Students have to complete 9 modules, covering research methodology (e.g. statistics, mathematical modelling, experimental research), foundation courses (e.g. micro-economics, game theory), and research seminars on subjects such as strategy, marketing, innovation, and operations and technology management.

Students may also apply to do a dissertation, in lieu of 3 modules, or a short individual research project, in lieu of 1 module.

Students receive qualitative feedback on their assessed performance and suggestions for improving their performance on the courses. Several courses have mid-term assessments to allow students to track their knowledge of the subject. Students are also welcome to approach lecturers for informal advice and guidance.

Assessment

- If dissertation option is chosen: 12,000 words.
- Short module on individual research project if chosen; 4000 words.

Assessment across the nine courses will be by written examination, project, or coursework, depending upon the nature of the particular course.

Faculty of Mathematics courses offered on the MPhil in SMO as electives are assessed exclusively by written examination.

Presentation features as an assessment component in a number of CJBS modules offered on the MPhil in SMO.

Continuing

Following their application for PhD continuation by the end of the first term, students will be interviewed by a panel of faculty members early in the second term. The PhD admissions committee will make PhD admission decisions on the basis of the interview report, the strength of the overall application, with particular weight on academic reference letters and the performance in the Michaelmas term MPhil courses, and the fit of the student's research interest with faculty expertise. Students will receive confirmation of a PhD offer in February. All admitted PhD students are fully funded. PhD offers are usually conditional on the final performance on the MPhil.

How to apply: http://www.graduate.study.cam.ac.uk/applying

Funding Opportunities

ESRC scholarships (1+3, including 3 years of PhD funding).

Lyondell Basell scholarship in Operations (1+3, including 3 years of PhD funding).

General Funding Opportunities http://www.graduate.study.cam.ac.uk/finance/funding

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Accurate and efficient scientific computations lie at the heart of most cross-discipline collaborations. It is key that such computations are performed in a stable, efficient manner and that the numerics converge to the true solutions, dynamics of the physics, chemistry or biology in the problem.

The programme closely follows the structure of our Applied Mathematical Sciences MSc and will equip you with the skill to perform efficient accurate computer simulations in a wide variety of applied mathematics, physics, chemical and industrial problems.

Students will take a total of 8 courses, 4 in each of the 1st and 2nd Semesters followed by a 3-month Project in the summer. A typical distribution for this programme is as follows:

Core courses

Modelling and Tools;
Stochastic Simulation;
Applied Linear Algebra;
Numerical Analysis;

Optional Courses

Dynamical Systems;
Optimization;
Partial Differential Equations;
Numerical Analysis of ODEs;
Applied Mathematics;
Statistical Methods;
Functional Analysis;
Software Engineering Foundations;
Mathematical Biology and Medicine;
Biologically Inspired Computation;
Advanced Software Engineering;
Geometry;
Bayesian Inference;

Typical project subjects

Simulation of Granular Flow and Growing Sandpiles;
Finite Element Discretisation of ODEs and PDEs;
Domain Decomposition;
Computational Spectral Theory;
Mathematical Modelling of Crime;
Mathematical Modelling of Micro-electron Mechanical Systems.
Can we Trust Eigenvalues on a Computer?

The final part of the MSc is an extended project in computational mathematics, giving the opportunity to investigate a topic in some depth guided by leading research academics from our 5-rated mathematics and statistics groups.

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Overview

Financial Mathematics is a branch of Mathematics where advanced mathematical and statistical methods are developed for and applied to financial markets and financial management. Its main aims are to quantify and hedge risks in the financial marketplace.

Effective computational methods are crucial for the successful use of mathematical modelling in finance. The MSc in Financial and Computational Mathematics is designed to reflect this combination of knowledge and skills so that its graduates are well equipped to enter the competitive job markets of quantitative finance and related fields.

The course is focused on computational techniques and mathematical modelling used in the financial industry and on the required background in finance. The course is provided by the School of Mathematical Sciences with valuable input from the School of Economics. To ensure that the degree keeps pace with changes in employer expectations and employment opportunities, the course has its own advisory board which consists of leading experts from the financial industry and academia.

Key facts:

- The School of Mathematical Sciences is one of the largest and strongest mathematics departments in the UK, with over 60 full-time academic staff.
- In the latest independent Research Assessment Exercise, the school ranked 8th in the UK in terms of research power across the three subject areas within the School of Mathematical Sciences (pure mathematics, applied mathematics, statistics and operational research).
- In the last independent Teaching Quality Assessment, the School scored 23 out of 24.
- The course has its own advisory board (see below) consisting of leading experts from the financial industry and academia.
- The course is offered in collaboration with the School of Economics.

Module details

Core modules include: financial mathematics, advanced financial mathematics, scientific computing and c++, advanced scientific computing, financial mathematics dissertation.

Optional Stream 1 (Maths/Stats and Computing): Optimisation, Time Series and Forecasting, Statistical Foundations.

Optional Stream 2A: Econometic Theory, Financial and Macro Econometrics, Time Series Econometrics, Mathematics for Engineering Management, Game Theory.

Optional Stream 2B: Microeconomic Analysis, Financial Economics, Options and Futures Markets, Mathematics for Engineering Management, Game Theory.

English language requirements for international students

IELTS: 6.5 (with no less than 6.0 in any element)

Further information

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What do Facebook, the financial system, Internet or the brain have in common?

All are connected in a network. From the underlying skeleton of social relations, the interdependent evolution of our financial system, to the emergent collective computation in the brain, most of the complex systems that appear in society, technology, and nature are ultimately characterised by a nontrivial pattern of inter-relations. This underlying architecture is in turn shaping how information diffuses and spreads, how resilient the system is against attacks or perturbations, or how complex patterns emerge at the systemic level from the aggregation of seemingly simple individuals.

Our MSc in Network Science will provide a thorough grounding in the core principles of modelling and analysis of complex and networked systems, along with the principal analytical and numerical methodologies. This will open to students a host of career opportunities in systems and networks modelling industries, spanning the IT, financial, and biomedical sectors, that are now requiring such specialist knowledge and skills.

Network Science is a very active and rapidly evolving research field with high societal impact, which stands at the crossroads of graph theory, complexity and data analysis. Addressing the description and modelling of the architecture and dynamics of complex systems -systems composed by many interacting units that show collective behaviour- it stands as a new kind of science to cope with some of the most challenging endeavours we face today, in an ever increasingly more connected society.
Its impact and applications outside academia pervades technological sectors such as communications and infrastructures (Internet, transportation networks, energy networks, urban mobility), biostatistics and network biology (brain modelling, protein interaction networks, postgenomic era), public health (epidemic spreading models), marketing and IT (social media, data analytics) to cite a few. This specialist masters programme aims at providing graduate students and professionals with a rigorous training in the underlying mathematical concepts, the analysis and modelling of complex networks and networked systems, complemented with training in computing, numerical simulations and massive data analysis. It is aimed towards students whose undergraduate degree is in mathematics or a cognate discipline who wish to enter a career involving analysis and optimisation of diverse kinds of networks, networked dynamics and models.

Why study your MSc Network Science at Queen Mary?
This is a pioneering MSc in the UK, a joint programme, taught by our Schools of Mathematical Sciences, and Electronic Engineering and Computer Science, drawing on their strengths in research and teaching in the area of complex networks, mathematical modelling of complex systems, and data mining.

We teach what we know and what we do best. Within the School of Mathematics, the Complex Systems & Networks group is one of the biggest hubs in Network Science within the UK, where we address both fundamental and applied challenges in the mathematical modelling of complex systems with clear societal impact, in collaboration with several industrial stakeholders. Within the School of Electronic Engineering, the Networks group was founded in 1987, and has hugely expanded ever since, bringing their expertise in online social networks, data mining and cloud computing. The coalescence of both groups expertises has fostered the creation of this unique MSc.

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Mission and goals

The objective of this programme of study is to prepare professionals able to deal with complex systems using sophisticated mathematical tools, yet with an engineering attitude. It harmonises a solid scientific background with a command of advanced methodologies and technologies. The programme is characterised by a continuous synergy between Applied Mathematics and Engineering disciplines- The students may choose among three specialisations:
- Computational Science and Engineering
- Applied Statistics
- Quantitative Finance

See the website http://www.polinternational.polimi.it/educational-offer/laurea-magistrale-equivalent-to-master-of-science-programmes/mathematical-engineering/

Career opportunities

The professional opportunities offered by this course are rather ample and varied: engineering consultancy companies that deal with complex computational problems; manufacturing or civil engineering companies where analyses based on the use of advanced mathematical tools are needed; banks, insurance companies and financial institutions making use of quantitative finance for risk analysis or forecast; companies that require statistical interpretation and the processing of complex data, or the simulation of different scenarios; public and private research institutes and laboratories.

Eligible students

Students holding a Bachelor degree in Mathematical Engineering, or in a related area with a solid background in the core disciplines of the programme, i.e. Applied Mathematics, Computer Science, Applied Physics or other Engineering disciplines are eligible for application. In particular, eligible students' past studies must include courses in different areas of Engineering (among Informatics, Economics & Business Organization, Electrotechnics, Automation, Electronics, Applied Physics, Civil Engineering) for at least 25% of the overall courses, as well as courses in different areas of Mathematics (Mathematical Analysis, Linear Algebra, Geometry, Probability, Statistics, Numerical Analysis, Optimization) for at least 33% of the overall courses.
The following tracks are available:
1. Computational Science and Engineering
2. Applied Statistics
3. Quantitative Finance

Eligible students must clearly specify the track they are applying for in their motivation letter.

Presentation

See http://www.polinternational.polimi.it/uploads/media/Mathematical_Engineering.pdf
The Master of Science in Mathematical Engineering (MSME) aims to form an innovative and flexible professional profile, endowed with a wide spectrum of basic scientific notions and engineering principles, together with a deep knowledge of modern pure and applied mathematical techniques. MSME is characterized by a continuous synergy between Mathematics and Engineering methods, oriented to the modelling, analysis and solution of complex planning, control and management problems, and provides the students with the possibility to face problems from various scientific, financial and/or technological areas. The MSME graduates can find employment in Engineering companies specialized in handling complex computational problems, requiring a multidisciplinary knowledge; in companies manufacturing industrial goods for which design analysis based on the use of advanced mathematical procedures are required; in service societies, banks, insurance companies, finance or consultant agencies for the statistical interpretation and the simulation of complex situations related to the analysis of large number of data (e.g. management and optimization of services, data mining, information retrieval) or for handling financial products and risk management; in public and private institutions. The programme is taught in English.

Subjects

Three main tracks available:
1. Computational Science for Engineering
Real and functional analysis; algorithms and parallel programming; numerical and theoretical analysis for partial differential equations; fluid mechanics; computational fluid dynamics advanced programming techniques for scientific computing;

2. Statistics
Real and functional analysis; algorithms and parallel programming; stochastic dynamical models; applied statistics, model identification and data analysis; Bayesian statistics

3. Mathematical Finance
Real and functional analysis; algorithms and parallel programming; stochastic differential equations; mathematical finance; financial engineering; model identification and data analysis.

In the motivation letter the student must clearly specify the track he/she is applying for.

See the website http://www.polinternational.polimi.it/educational-offer/laurea-magistrale-equivalent-to-master-of-science-programmes/mathematical-engineering/

For contact information see here http://www.polinternational.polimi.it/educational-offer/laurea-magistrale-equivalent-to-master-of-science-programmes/mathematical-engineering/

Find out how to apply here http://www.polinternational.polimi.it/how-to-apply/

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What do Facebook, the financial system, Internet or the brain have in common?

"Everything is connected, all is network"
From the underlying skeleton of social relations, the interdependent evolution of our financial system, to the emergent collective computation in the brain, most of the complex systems that appear in society, technology, and nature are ultimately characterised by a nontrivial pattern of inter-relations. This underlying architecture is in turn shaping how information diffuses and spreads, how resilient the system is against attacks or perturbations, or how complex patterns emerge at the systemic level from the aggregation of seemingly simple individuals.

Our MSc Network Science will provide a thorough grounding in the core principles of modelling and analysis of complex and networked systems, along with the principal analytical and numerical methodologies. This will open to students a host of career opportunities in systems and networks modelling industries, spanning the IT, financial, and biomedical sectors, that are now requiring such specialist knowledge and skills.

Network Science is a very active and rapidly evolving research field with high societal impact, which stands at the crossroads of graph theory, complexity and data analysis. Addressing the description and modelling of the architecture and dynamics of complex systems -systems composed by many interacting units that show collective behaviour- it stands as a new kind of science to cope with some of the most challenging endeavours we face today, in an ever increasingly more connected society.
Its impact and applications outside academia pervades technological sectors such as communications and infrastructures (Internet, transportation networks, energy networks, urban mobility), finance (financial risk and systemic instability, financial networks, interbank cross-correlations), marketing and IT (social media, data analytics), public health (epidemic spreading models), or biostatistics and network biology (brain modelling, protein interaction networks, postgenomic era), to cite a few. This specialist masters programme aims at providing graduate students and professionals with a rigorous training in the underlying mathematical concepts, the analysis and modelling of complex networks and networked systems, complemented with training in computing, numerical simulations and massive data analysis. It is aimed towards students with a mathematical background who wish to enter a career involving analysis and optimisation of diverse kinds of networks, networked dynamics and models.

Why study your MSc Network Science at Queen Mary?
This is a pioneering MSc in the UK, a joint programme, taught by our Schools of Mathematical Sciences, and Electronic Engineering and Computer Science, drawing on their strengths in research and teaching in the area of complex networks, mathematical modelling of complex systems, and data mining.

We teach what we know and what we do best. Within the School of Mathematics, the Complex Systems & Networks group is one of the biggest hubs in Network Science within the UK, where we address both fundamental and applied challenges in the mathematical modelling of complex systems with clear societal impact, in collaboration with several industrial stakeholders. Within the School of Electronic Engineering, the Networks group was founded in 1987, and has hugely expanded ever since, bringing their expertise in online social networks, data mining and cloud computing. The coalescence of both groups expertises has fostered the creation of this unique MSc.

More about our two schools

Queen Mary is a member of the prestigious Russell Group of leading UK universities, combining world-class research, teaching excellence and unrivalled links with business and the public sector. The School of Mathematical Sciences has a distinguished history on itself. We have been conducting pioneering mathematical research since the 1950s, and as one of the largest mathematical departments in the UK, with over 50 members of staff, the school can offer diverse postgraduate study opportunities across the field, from pure and applied mathematics, to finance and statistics. Along with the MSc in Network Science, our cohort of postgraduate students specialise in Mathematics and Statistics, Mathematical Finance and Financial Computing. We are one of the UK’s leading universities in the most recent national assessment of research quality, we were placed ninth in the UK (REF 2014) amongst multi-faculty universities. This means that the teaching on our postgraduate programmes is directly inspired by the world-leading research of our academics. Our staff includes international leaders in many areas of mathematical research, and the School is a hive of activity, providing a vibrant intellectual space for postgraduate study.

The School of Electronic Engineering and Computer Science is internationally recognised for their pioneering and ground-breaking research in several areas including machine learning and applied network analysis. This expertise uniquely complements the more theoretical knowledge offered by the School of Mathematical Sciences, providing a well balanced mix of theory and applications and offering a deep and robust programme that combines the foundations of the mathematics of networks with the latest cutting edge applications in real world problems.

Additionally, Queen Mary holds a university-level Bronze Award for the Athena SWAN Charter, which recognises and celebrates good employment practice for women working in mathematics, science, engineering and technology in higher education and research.

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