Masters Degrees (Scientific Computing)

Visit our website for more information on fees, scholarships, postgraduate loans and other funding options to study High Performance and Scientific Computing at Swansea University - 'Welsh University of the Year 2017' (Times and Sunday Times Good University Guide 2017).

The MSc in High Performance and Scientific Computing is for you if you are a graduate in a scientific or engineering discipline and want to specialise in applications of High Performance computing in your chosen scientific area. During your studies in High Performance and Scientific Computing you will develop your computational and scientific knowledge and skills in tandem helping emphasise their inter-dependence.

On the course in High Performance and Scientific Computing you will develop a solid knowledge base of high performance computing tools and concepts with a flexibility in terms of techniques and applications. As s student of the MSc High Performance and Scientific Computing you will take core computational modules in addition to specialising in high performance computing applications in a scientific discipline that defines the route you have chosen (Biosciences, Computer Science, Geography or Physics). You will also be encouraged to take at least one module in a related discipline.

Modules of High Performance and Scientific Computing MSc

The modules you study on the High Performance and Scientific Computing MSc depend on the route you choose and routes are as follows:

Biosciences route (High Performance and Scientific Computing MSc):

Graphics Processor Programming

High Performance Computing in C/C++

Operating Systems and Architectures

Software Testing

Programming in C/C++

Conservation of Aquatic Resources or Environmental Impact Assessment

Ecosystems

Research Project in Environmental Biology

+ 10 credits from optional modules

Computer Science route (High Performance and Scientific Computing MSc):

Graphics Processor Programming

High Performance Computing in C/C++

Operating Systems and Architectures

Software Testing

Programming in C/C++

Partial Differential Equations

Numerics of ODEs and PDEs

Software Engineering

Data Visualization

MSc Project

+ 30 credits from optional modules

Geography route (High Performance and Scientific Computing MSc):

Graphics Processor Programming

High Performance Computing in C/C++

Operating Systems and Architectures

Software Testing

Programming in C/C++

Partial Differential Equations

Numerics of ODEs and PDEs

Modelling Earth Systems or Satellite Remote Sensing or Climate Change – Past, Present and Future or Geographical Information Systems

Research Project

+ 10 credits from optional modules

Physics route (High Performance and Scientific Computing MSc):

Graphics Processor Programming

High Performance Computing in C/C++

Operating Systems and Architectures

Software Testing

Programming in C/C++

Partial Differential Equations

Numerics of ODEs and PDEs

Monte Carlo Methods

Quantum Information Processing

Phase Transitions and Critical Phenomena

Physics Project

+ 20 credits from optional modules

Optional Modules (High Performance and Scientific Computing MSc):

Software Engineering

Data Visualization

Monte Carlo Methods

Quantum Information Processing

Phase Transitions and Critical Phenomena

Modelling Earth Systems

Satellite Remote Sensing

Climate Change – Past, Present and Future

Geographical Information Systems

Conservation of Aquatic Resources

Environmental Impact Assessment

Ecosystems

Facilities

Students of the High Performance and Scientific Computing programme will benefit from the Department that is well-resourced to support research. Swansea physics graduates are more fortunate than most, gaining unique insights into exciting cutting-edge areas of physics due to the specialized research interests of all the teaching staff. This combined with a great staff-student ratio enables individual supervision in advanced final year research projects. Projects range from superconductivity and nano-technology to superstring theory and anti-matter. The success of this programme is apparent in the large proportion of our M.Phys. students who seek to continue with postgraduate programmes in research.

Specialist equipment includes:

a low-energy positron beam with a highfield superconducting magnet for the study of positronium

a number of CW and pulsed laser systems

scanning tunnelling electron and nearfield optical microscopes

a Raman microscope

a 72 CPU parallel cluster

access to the IBM-built ‘Blue C’ Supercomputer at Swansea University and is part of the shared use of the teraflop QCDOC facility based in Edinburgh

The Physics laboratories and teaching rooms were refurbished during 2012 and were officially opened by Professor Lyn Evans, Project Leader of the Large Hadron Collider at CERN. This major refurbishment was made possible through the University’s capital programme, the College of Science, and a generous bequest made to the Physics Department by Dr Gething Morgan Lewis FRSE, an eminent physicist who grew up in Ystalyfera in the Swansea Valley and was educated at Brecon College.

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The MPhil programme in Scientific Computing is a full-time 12-month course which aims to provide education of the highest quality at Master’s level. Covering topics of high-performance scientific computing and advanced numerical methods and techniques, it produces graduates with rigorous research and analytical skills, who are well-equipped to proceed to doctoral research or directly into employment in industry, the professions, and the public service. It also provides training for the academic researchers and teachers of the future, encouraging the pursuit of research in computational methods for science and technology disciplines, thus being an important gateway for entering PhD programmes containing a substantial component of computational modelling.

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

Course detail

The MPhil in Scientific Computing has a research and a taught element. The research element is a project on a science or technology topic which is studied by means of scientific computation. The taught element comprises of core lecture courses on topics of scientific computing and elective lecture courses relevant to the science or technology topic of the project. Most of the projects are expected to make use of the University’s High Performance Computing Service.

The students will attend lecture courses during Michaelmas Term (some courses may be during Lent Term) and then they will undertake a substantial Research Project over the next 6 months (from March to the end of August) in a participating Department. The research element aims to provide essential skills for continuation to a PhD programme or employment, as well as to assess and enhance the research capacity of the students. It is based on a science or technology topic which is studied by means of scientific computation. Research project topics will be provided by academic supervisors or by the industrial partners who are working with the participating Departments and may be sponsoring the research project.

There is equal examination credit weighting between the taught and the research elements of the course, which is gained by submitting a dissertation on the project and by written assignments and examinations on the core and elective courses, respectively.

Weighting of the assessed course components is as follows: Dissertation (research) 50%; written assignments on the core courses 25%; written examinations on the elective courses 25%.

Learning Outcomes

By the end of the course, students will have:

- a comprehensive understanding of numerical methods, and a thorough knowledge of the literature, applicable to their own research;
- demonstrated originality in the application of knowledge, together with a practical understanding of how research and enquiry are used to create and interpret knowledge in their field;
- shown abilities in the critical evaluation of current research and research techniques and methodologies;
- demonstrated self-direction and originality in tackling and solving problems, and acted autonomously in the planning and implementation of research.

Format

The taught element comprises core lecture courses on topics of all aspects of scientific computing, and elective lecture courses relevant to the topic of the research project.

The taught element comprises core lecture courses on topics of all aspects of scientific computing, and elective lecture courses relevant to the topic of the research project. There is equal examination credit weighting between the taught and the research elements of the course, which is gained by submitting a dissertation on the project and by written assignments and examinations on the core and elective courses, respectively. Weighting of the assessed course components is as follows: Dissertation (research) 50%; written assignments 25%; written examinations 25%.

The core lectures are on topics of high performance scientific computing numerical analysis and advanced numerical methods and techniques. They are organized by the Centre for Scientific Computing and are taught and examined during the first five months (October-February). Their purpose is to provide the students with essential background knowledge for completing their dissertation and for their general education in scientific computing.

In particular, their objective is to introduce students to the simulation science pipeline of problem identification, modelling, simulation and evaluation - all from the perspective of employing high-performance computing. Numerical discretisation of mathematical models will be a priority, with a specific emphasis on understanding the trade-offs (in terms of modelling time, pre-processing time, computational time, and post-processing time) that must be made when solving realistic science and engineering problems. Understanding and working with computational methods and parallel computing will be a high priority. To help the students understand the material, the lecturers will furnish the courses with practical coursework assignments.

The lectures on topics of numerical analysis and HPC are complemented with hands-on practicals using Linux-based laptops provided by the course (students may bring their own), as well as on the University’s High Performance Computing Service.

Appropriate elective lecture courses are selected from Master’s-level courses offered by the Departments of the School of Physical Sciences, Technology or Biological Sciences. The choice of courses will be such as to provide the students with essential background knowledge for completing their theses and for their general education in the materials science application of the project. They are decided in consultation with the project supervisor. While every effort is made within the Departments to arrange the timetable in a coherent fashion, it is inevitable that some combinations of courses will be ruled out by their schedule, particularly if the choices span more than one department.

Continuing

For continuation to a PhD programme in Scientific Computing, students are required to gain a Distinction (overall grade equal or greater than 75%).

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

Funding Opportunities

There are no specific funding opportunities advertised for this course. For information on more general funding opportunities, please follow the link below.

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

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Scientists and engineers are tackling ever more complex problems, most of which do not admit analytical solutions and must be solved numerically. Numerical methods can only play an even more important role in the future as we face even bigger challenges. Therefore, skilled scientific programmers are in high demand in industry and academia and will drive forward much of the future economy.

About this degree

This programme aims to provide a rigorous formal training in computational science to produce highly computationally skilled scientists and engineers capable of applying numerical methods and critical evaluation of their results to their field of science or engineering. It brings together best practice in computing with cutting-edge science and provides a computing edge over traditional science, engineering and mathematics programmes.

Students undertake modules to the value of 180 credits.

The programme consists of six core modules (90 credits), two optional modules (30 credits) and a dissertation/report (60 credits).

A Postgraduate Diploma, six core modules (90 credits), two optional modules (30 credits), is also offered.

Core modules

• Computational and Simulation Methods
• Numerical Methods
• Numerical Optimisation
• Research Computing with C++
• Research Software Engineering with Python
• Techniques of High-Performance Computing

Optional modules

Options include a wide selection of modules across UCL Engineering and UCL Mathematical & Physical Sciences.

Dissertation/report

All students undertake an independent research project project which culminates in a dissertation of 20,000 words.

Teaching and learning

The programme is delivered through a combination of lectures and hands-on programming and includes a variety of short programming projects, delivered as part of the taught component. Students are encouraged to participate in scientific seminars, for example, weekly seminars at the UCL Centre for Inverse Problems. Assessment is through examinations, assignments, small projects and the dissertation, including a computer programme.

Further information on modules and degree structure is available on the department website: Scientific Computing MSc

Funding

For a comprehensive list of the funding opportunities available at UCL, including funding relevant to your nationality, please visit the Scholarships and Funding website.

Careers

We expect our graduates to take up exciting science and engineering roles in industry and academia with excellent prospects for professional development and steep career advancement opportunities. This degree enables students to work on cutting-edge real-life problems, overcome the challenges they pose and so contribute to advancing knowledge and technology in our society.

Employability

Students develop a comprehensive set of skills which are in high demand both in industry and academia: professional software development skills including state-of-the-art scripting and compiled languages; knowledge of techniques used in high-performance computing; understanding and an ability to apply a wide range of numerical methods and numerical optimisation; a deeper knowledge of their chosen science subject; oral and written presentational skills.

Why study this degree at UCL?

UCL has a global reputation for excellence in research and is committed to delivering impact and innovations that enhance the lives of people in the UK, across Europe and around the world. UCL is consistently placed in the global top 20 across a wide range of university rankings (currently 7th in QS World University Rankings 2018). Furthermore, the Thomson Scientific Citation Index shows that UCL is the second-most highly cited European university and 12th in the world.

Our wide-ranging expertise provides opportunities for groundbreaking interdisciplinary investigation. World-leading experts in the field and students benefit from a programme of distinguished visitors and guest speakers in many scientific seminars. In this way a network of collaborators, mentors and peers is created, which students can access in their future career.

This degree has been designed to balance a professional software development and high performance computing skills with a comprehensive selection of numerical mathematics and scientific subjects, culminating in a scientific computing dissertation project. The dual aspect of a science and computing degree enables students to tackle real-life problems in a structured and rigorous way and produce professional software for their efficient solution.

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As an interdisciplinary centre we offer relevant modules run by other departments as well as the Centre’s own modules. The list of modules offered is reviewed annually and is dependent on those offered by each department.

This MSc provides an exciting mixture of fundamental methods and cutting-edge applications of Scientific Computing. Formal training covers software engineering for both workstations and high-performance computers, and underpinning algorithms. You may then choose a project within one of the many interdisciplinary Scientific Computing research groups at Warwick.

Our interdisciplinary focus gives you access to a breadth of expertise across the natural sciences at Warwick. You will benefit from extra contact time with staff and other students, while also being able to work across departments in accordance with your subject interests. Recent graduate destinations include the financial and IT sector. A large proportion of our MSc students continue on to a PhD programme in a science discipline and others have advanced to roles in IT development, modelling and consultancy.

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This one-year master's course provides training in the application of mathematics to a wide range of problems in science and technology. Emphasis is placed on the formulation of problems, on the analytical and numerical techniques for a solution and the computation of useful results.

By the end of the course students should be able to formulate a well posed problem in mathematical terms from a possibly sketchy verbal description, carry out appropriate mathematical analysis, select or develop an appropriate numerical method, write a computer program which gives sensible answers to the problem, and present and interpret these results for a possible client. Particular emphasis is placed on the need for all these parts in the problem solving process, and on the fact that they frequently interact and cannot be carried out sequentially.

The course consists of both taught courses and a dissertation. To complete the course you must complete 13 units.

There are four core courses which you must complete (one unit each), which each usually consist of 24 lectures, classes and an examination. There is one course on mathematical methods and one on numerical analysis in both Michaelmas term and Hilary term. Each course is assessed by written examination in Week 0 of the following term.

Additionally, you must choose at least least one special topic in the area of modelling and one in computation (one unit each). There are around twenty special topics to choose from, spread over all three academic terms, each usually consisting for 12 to 16 lectures and a mini project, which culminates in a written report of around 20 pages. Topics covered include mathematical biology, fluid mechanics, perturbation methods, numerical solution of differential equations and scientific programming. 

You must also undertake at least one case study in modelling and one in scientific computing (one unit each), normally consisting of four weeks of group work, an oral presentation and a report delivered in Hilary term.

There is also a dissertation (four units) of around 50 pages, which does not necessarily need to represent original ideas. Since there is another MSc focussed on mathematical finance specifically, the MSc in Mathematical and Computational Finance, you are not permitted to undertake a dissertation in this field.

You will normally accumulate four units in core courses, three units in special topics, two units in case studies and four units in the dissertation. In addition, you will usually attend classes in mathematical modelling, practical numerical analysis and additional skills during Michaelmas term.

In the first term, students should expect their weekly schedule to consist of around seven hours of core course lectures and seven hours of modelling, practical numerical analysis and additional skills classes, then a further two hours of lectures for each special topic course followed. In addition there are about three hours of problem solving classes to go through core course exercises and students should expect to spend time working through the exercises then submitting them for marking prior to the class. There are slightly fewer contact hours in the second term, but students will spend more time working in groups on the case studies.

In the third term there are some special topic courses, including one week intensive computing courses, but the expectation is that students will spend most of the third term and long vacation working on their dissertations. During this time, students should expect to work hours that are equivalent to full-time working hours, although extra hours may occasionally be needed. Students are expected to write special topic and case study reports during the Christmas and Easter vacations, as well as revising for the core course written examinations.

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Overview

The development of new materials lies at the heart of many of the technological challenges we currently face, for example creating advanced materials for energy generation. Computational modelling plays an increasingly important role in the understanding, development and optimisation of new materials. This four year Doctoral Training Programme on computational methods for material modelling aims to train scientists not only in the use of existing modelling methods but also in the underlying computational and mathematical techniques. This will allow students to develop and enhance existing methods, for instance by introducing new capabilities and functionalities, and also to create innovative new software tools for materials modelling in industrial and academic research. The first year of the CDT is a materials modelling option within the MPhil in Scientific Computing (please see the relevant entry) at the University of Cambridge and a range of additional training elements.

The MPhil in Scientific Computing is administered by the Department of Physics, but it serves the training needs of the Schools of Physical Sciences, Technology and Biological Sciences. The ability to have a single Master’s course for such a broad range of disciplines and applications is achieved by offering core (i.e. common for all students) numerical and High Performance Computing (HPC) lecture courses, and complementing them with elective courses relevant to the specific discipline applications.

In this way, it is possible to generate a bespoke training portfolio for each student without losing the benefits of a cohort training approach. This bespoke course is fully flexible in allowing each student to liaise with their academic or industrial supervisor to choose a study area of mutual interest.

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

Learning Outcomes

By the end of the course, students will have:
- a comprehensive understanding of numerical methods, and a thorough knowledge of the literature, applicable to their own research;
- demonstrated originality in the application of knowledge, together with a practical understanding of how research and enquiry are used to create and interpret knowledge in their field;
- shown abilities in the critical evaluation of current research and research techniques and methodologies;
- demonstrated self-direction and originality in tackling and solving problems, and acted autonomously in the planning and implementation of research.

Teaching

The first year of the CDT has a research as well as a taught element. The students attend lecture courses during the first five months (October-February) and then they will undertake a substantial Research Project over the next 6 months (from March to the end of August) in a participating Department. The research element aims to provide essential skills for a successful completion of the PhD, as well as to assess and enhance the research capacity of the students. It is based on a materials science topic which is studied by means of scientific computation. Research project topics will be provided by academic supervisors or by the industrial partners. Most of the projects are expected to make use the University’s High Performance Computing Service (for which CPU time for training and research has been budgeted for every student).

The taught element comprises core lecture courses on topics of all aspects of scientific computing, and elective lecture courses relevant to the topic of the research project. There is equal examination credit weighting between the taught and the research elements of the course, which is gained by submitting a dissertation on the project and by written assignments and examinations on the core and elective courses, respectively. Weighting of the assessed course components is as follows: Dissertation (research) 50%; written assignments 25%; written examinations 25%.

The core courses are on topics of high-performance scientific computing and advanced numerical methods and techniques; they are taught and examined during the first five months (October-February). Their purpose is to provide the students with essential background knowledge for completing their theses and for their general education in scientific computing.

Appropriate elective courses are selected from Master’s-level courses offered by the Departments of the School of Physical Sciences, Technology or Biological Sciences. The choice of courses will be such as to provide the students with essential background knowledge for completing their theses and for their general education in the materials science application of the project. They are decided in consultation with the project supervisor.

Depending on the materials science application of the research topic, students will follow one of the following two numerical methodology options: a) Continuum methods based on systems of partial differential equations (PDEs, e.g. finite-difference, element or volume methods); or b) atomistic approaches, which can be based on classical particle-based modelling (e.g. molecular dynamics) or on electronic structure- based methods (e.g. density functional theory). The students who take the atomistic modelling options will attend a 12-lecture course before continuing to classical particle-based methods or electronic structure methods. Irrespective of the numerical methodology option, students will attend lecture courses on High Performance Computing topics and elements of Numerical Analysis.

In addition to the comprehensive set of Masters-level courses provided by the MPhil and across the University in the field, which will be available to the CDT students, it will also be possible for students to take supplementary courses (not for examination) at undergraduate level, where a specific need is identified, in order to ensure that any prerequisite knowledge for the Masters courses is in place.

Moreover, depending on their background and circumstances, students may be offered places in the EPSRC-funded Autumn Academy, which takes place just before the start of the academic year (two weeks in September).

Funding Opportunities

Studentships funded by EPSRC and/or Industrial and other partners are available subject to eligibility criteria.

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

Find out how to apply here http://www.graduate.study.cam.ac.uk/courses/directory/pcphpdcms/apply

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

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Overview

Scientific computing is a new and growing discipline in its own right. It is concerned with harnessing the power of modern computers to carry out calculations relevant to science and engineering.

By its very nature, scientific computing is a fundamentally multidisciplinary subject. The various application areas give rise to mathematical models of the phenomena being studied.

Examples range in scale from the behaviour of cells in biology, to flow and combustion processes in a jet engine, to the formation and development of galaxies. Mathematics is used to formulate and analyse numerical methods for solving the equations that come from these applications.

Implementing the methods on modern, high performance computers requires good algorithm design to produce efficient and robust computer programs. Competence in scientific computing thus requires familiarity with a range of academic disciplines. The practitioner must, of course, be familiar with the application area of interest, but it is also necessary to understand something of the mathematics and computer science involved.

Whether you are interested in fundamental science, or a technical career in business or industry, it is clear that having expertise in scientific computing would be a valuable, if not essential asset. The question is: how does one acquire such expertise?

This course is one of a suite of MScs in Scientific Computation that are genuinely multidisciplinary in nature. These courses are taught by internationally leading experts in various application areas and in the core areas of mathematics and computing science, fully reflecting the multidisciplinary nature of the subject. The courses have been carefully designed to be accessible to anyone with a good first degree in science or engineering. They are excellent preparation either for research in an area where computational techniques play a significant role, or for a career in business or industry.

Key facts:

- This course is offered in collaboration with the School of Computer Science.

- It is one of a suite of courses focusing on scientific computation.

- 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).

Modules

Advanced Techniques for Differential Equations

Computational Linear Algebra

Operations Research and Modelling

Programming for Scientific Computation

Scientific Computation Dissertation

Simulation for Computer Scientists

Stochastic Financial Modelling

Variational Methods

Vocational Mathematics

Data Mining Techniques and Applications

Mathematical Foundations of Programming

English language requirements for international students

IELTS: 6.0 (with no less than 5.5 in any element)

Further information

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The Applied Mathematics group in the School of Mathematics at the University of Manchester has a long-standing international reputation for its research. Expertise in the group encompasses a broad range of topics, including Continuum Mechanics, Analysis & Dynamical Systems, Industrial & Applied Mathematics, Inverse Problems, Mathematical Finance, and Numerical Analysis & Scientific Computing. The group has a strongly interdisciplinary research ethos, which it pursues in areas such as Mathematics in the Life Sciences, Uncertainty Quantification & Data Science, and within the Manchester Centre for Nonlinear Dynamics.

The Applied Mathematics group offers the MSc in Applied Mathematics as an entry point to graduate study. The MSc has two pathways, reflecting the existing strengths within the group in numerical analysis and in industrial mathematics. The MSc consists of five core modules (total 75 credits) covering the main areas of mathematical techniques, modelling and computing skills necessary to become a modern applied mathematician. Students then choose three options, chosen from specific pathways in numerical analysis and industrial modelling (total 45 credits). Finally, a dissertation (60 credits) is undertaken with supervision from a member of staff in the applied mathematics group with the possibility of co-supervision with an industrial sponsor. 

Aims

The course aims to develop core skills in applied mathematics and allows students to specialise in industrial modelling or numerical analysis, in preparation for study towards a PhD or a career using mathematics within industry. An important element is the course regarding transferable skills which will link with academics and employers to deliver important skills for a successful transition to a research career or the industrial workplace.

Special features

The course features a transferable skills module, with guest lectures from industrial partners. Some dissertation projects and short internships will also be available with industry.

Teaching and learning

Students take eight taught modules and write a dissertation. The taught modules feature a variety of teaching methods, including lectures, coursework, and computing and modelling projects (both individually and in groups). The modules on Scientific Computing and Transferable Skills particularly involve significant project work. Modules are examined through both coursework and examinations.

Coursework and assessment

Assessment comprises course work, exams in January and May, followed by a dissertation carried out and written up between June and September. The dissertation counts for 60 credits of the 180 credits and is chosen from a range of available projects, including projects suggested by industrial partners.

Course unit details

Course unit details

 CORE (75 credits)

 * Introduction to Uncertainty Quantification

 * Mathematical Methods

 * Partial Differential Equations

 * Scientific Computing

 * Transferable Skills for Applied Mathematicians

 OPTIONAL (3 modules, 45 credits)

 * Applied Dynamical Systems (IM)

 * Continuum Mechanics (IM)

 * Stability theory (IM)

 * Transport Phenomena and Conservation Laws (IM)

 * Advanced Uncertainty Quantification (IM,NA)

 * Approximation Theory and Finite Element Analysis (NA)

 * Numerical Linear Algebra (NA)

 * Numerical Optimization and Inverse Problems (NA)

Students registered on the Numerical Analysis pathway must select modules marked NA, and those registered on the Industrial Modelling pathway must select modules marked IM.

Syllabuses for the modules Introduction to Uncertainty Quantification and Advanced Uncertainty Quantification are currently being finalized and details will be added here as soon as possible.

Facilities

Modern computing facilities are available to support the course.

Disability support

Practical support and advice for current students and applicants is available from the Disability Advisory and Support Service. Email: [email protected]

Career opportunities

The programme will prepare students for a career in research (via entry into a PhD programme) or direct entry into industry. Possible subsequent PhD programmes would be those in mathematics, computer science, or one of the many science and engineering disciplines where applied mathematics is crucial. The programme develops many computational, analytical, and modelling skills, which are valued by a wide range of employers. Specialist skills in scientific computing are valued in the science, engineering, and financial sector.

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This unique programme provides numerate graduates with the requisite expertise for the development of a professional career in the profitable and intellectually exciting triangle formed by mathematics, technology and finance.

Financial institutions rely on a functional blend of Mathematics, Technology and Finance to develop, enhance and sustain their competitive edge. The financial industry is undergoing a second wave of technological transformation related in particular to: the establishment of electronic trading platforms; improved risk management and pricing accuracy; the high performance computing implications of expanding regulatory requirements.

As a result there is increasing demand for numerate and technologically capable personnel from a wide range of top employers including investment banks, hedge funds, financial software companies, brokerage firms and consultancy firms. Other business lines are now developing similar paradigms where numerate, technologically able personnel are part of business innovation and decision-making.

The Financial Computing MSc is run jointly by the School of Mathematical Sciences and the School of Electronic Engineering and Computer Science. It is aimed at science and engineering graduates with mathematical exposure and some experience in computer programming. The content of the programme is a combination of technology and financial mathematics. It contains modules related to up-to-the-minute industry challenges such as high performance and GPU development.

Why study with us?

• Queen Mary is a member of the prestigious Russell Group of leading UK universities, combining world-class research and teaching excellence.
• You will be taught by distinguished academics and experienced practitioners who blend advanced theory with practical applications.
• You will study in recently refurbished MSc student offices, with state-of-the-art computers and software.
• We are conveniently located in central London, in close proximity to the two world renowned financial districts of the City of London and Canary Wharf.

Programme Outline

The study programme consists of four compulsory and four elective modules. The modules offered by the School of Mathematical Sciences will provide a solid understanding of the principles of mathematical finance. The modules offered within the Schools of Electronic Engineering and Computer Sciences will focus on key aspects of technological implementation.

Full time Study

You will study eight modules in total with an even split across semesters one and two. You will complete a 10,000 word dissertation/research project during semester three.

Full time Study with Industrial Experience

You will study eight modules in total with an even split across semesters one and two. You will complete a 10,000 word dissertation/research project during semester three. Expert staff will support the arrangement of your industrial placement, which will be carried out in the second year of your programme and assessed through the completion of the Industrial Placement Project.

The industrial placement takes place from the September following the taught part of the MSc and is for a maximum of 12 months. It is a student's responsibility to secure their own placement, but the EECS Placement Team will provide support. The Placement Team source and promote suitable opportunities, assist with applications, and with interview preparation.

The industrial placement consists of 8-12 months spent working with an appropriate employer in a role that relates directly to your field of study. The placement is currently undertaken after you have completed and passed the taught component of the degree and submitted your MSc project. The placement will provide you with the opportunity to apply the key technical knowledge and skills that you have learnt in your taught modules, and will enable you to gain a better understanding of your own abilities, aptitudes, attitudes and employment potential. The module is only open to students enrolled on a programme of study with integrated placement.

In the event that you are unable to secure a placement we will transfer you onto the 1 year FT taught programme without the Industrial Experience. This change will also apply to any student visa you hold at the time.

Part time Study

Your programme is delivered across two academic years. You will study four modules in each year of the programme, registering upon two modules per semester to balance your workload.

Our modules are assessed by a mixture of in-term assessment and final examinations. Examinations are held between late April and early June. Dissertations are evaluated in September. Successful completion of the MSc programme will result in the award of the MSc Financial Computing (possibly with Merit or with Distinction).

Structure

Semester 1 - Compulsory

• ECS793P Introduction to Object-Oriented Programming
• MTH771P Foundations of Mathematical Modelling in Finance
• MTH739N Topics in Scientific Computing

Semester 1 - Elective

• ECS765P Functional Programming
• ECS765P Big Data Processing
• ECS708P Machine Learning

Semester 2 - Compulsory

• MTH777P Financial Programming

Semester 2 - Elective

• MTH773P Advanced Computing in Finance
• ECS769P Advanced Object-Oriented Programming
• ECS786P Parallel Computing
• MTH774P Portfolio Theory and Risk Management
• MTH772P Stochastic Calculus and Black Scholes Theory

The Project

Each MSc Financial Computing student is required to complete a 60 credit project dissertation. A typical MSc project dissertation consists of about 30 word-processed pages (10,000 words), securely bound, covering a specific research-level topic in financial computing, usually requiring the student to understand, explain and elaborate on results from one or more journal articles and possibly to implement some industry quality code.

Detailed outlines of each module for MSc Financial Computing are on Queen Mary University of London website.

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Applicants to this programme are numerate and logically-minded, and it is likely that they will have previously studied software engineering, programming, computer science, maths or physics. Such students will seek more specialised, technical programming and software engineering skills. They would learn in-depth, a range of programming concepts, languages and software development techniques to develop sophisticated and complex programs. Graduates will seek positions as software developers, systems engineers, software testers, programmers, etc.

Course Overview

The main themes of the programme are:
-Current and emerging Software Engineering principles and practices
-Current and emerging programming practices
-Large scale software project management

This programme will equip students with those skills at a high academic level and also crucially enable them to practically implement their knowledge because of the ‘hands-on’ emphasis of the programme.

The Current and emerging Software Engineering principles and practices includes aspects of generic programming.

The Current and emerging programming practices theme covers advanced topics in modules such as Generic Programming and aspects of Scientific Computing and Virtualisation

The Large scale project management theme will concentrate on management and systems analysis skills to be developed by the students both of which are in great demand by employers.

Modules

Part 1
-Agile Software Development (20 credits)
-Generic Programming (20 credits)
-Leadership and Management (20 credits)
-Managing Information Systems and Projects (20 credits)
-Research Methods and Data Analysis (20 credits)
-Scientific Computing & Virtualisation (20 credits)

Part 2
-Major Project (60 credits)

Key Features

Software Engineering as a subject evolved from a desire to incorporate engineering practices including, analysis, design, testing and project management to the process of creating computer programs. As a discipline, software engineering is as broad as the software created, with applications as diverse as plant control (real-time critical systems) to commercial trading software (database driven software) to image processing applications for mobile phones (computer graphics based mobile applications).

Assessment

The School of Applied Computing aims to produce graduates that help shape the future of computing and information systems development. The course content is contemporary and shaped for employability through close links with local and national employers.

Students are assessed through a combination of worksheets, practicals, presentations, projects and examinations. Module assessment is often by assignment, or assignment and examination. The final mark for some modules may include one or more pieces of course work set and completed during the module. Project work is assessed by written report and presentation.

Students are encouraged to use our links with Software Alliance Wales and Go Wales to work on commercial schemes for their Major Project module. Go Wales provides the opportunity of paid work placements with local businesses.

Career Opportunities

Graduates from this programme will be skilled and knowledgeable in the technical aspects of software development, and are likely to find employment either within specialist software organisations, or within organisations which commit resources to developing highly technical software systems. They are likely to have to work as a member of a team, conceiving, designing, developing and implementing complex software systems. Graduates from this programme would expect to be initially employed as software engineers. Those employed by SMEs are likely to work in smaller teams or perhaps as sole developers. Students finding employment with larger companies are most likely to work in larger teams building a variety of large-scale applications.

It is expected that graduates would seek positions such as:
-Software Engineers
-Senior software Engineers
-Software Developers
-Application Developers
-System Engineers
-Software Technical Lead
-System Analyst
-Version control manager
-Project lead/manager

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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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The MSc in Computational Finance will introduce students to the computational methods that are widely used by practitioners and financial institutions in today's markets. This will provide students with a solid foundation not only in traditional quantitative methods and financial instruments, but also scientific computing, numerical methods, high-performance computing, distributed ledgers, big-data analytics, and agent-based modelling. These techniques will be used to understand financial markets from a post-crisis perspective which incorporates findings from the study of financial markets at high-frequency time scales, modern approaches to understanding systematic risk and financial contagion, and disruptive technologies such as distributed-ledgers and crypto-currencies. The programme is highly practical, and students will have the opportunity to apply their learning to real-world data and case studies in hands-on laboratory sessions.

Key benefits

• An understanding of modern financial technology (FinTech) including electronic trading and distributed-ledger technology.
• Practical hands-on techniques for working with and analysing financial data, which draw on modern developments in Artificial Intelligence and Big Data technology.
• The opportunity to understand the practical aspects of quantitative finance and FinTech from Industry experts located in the heart of one of the World’s financial centres.

Description

Computational Finance studies problems of optimal investment, risk management and trade execution from a computational perspective. As with any engineering discipline, computational finance analyses a given problem by first building a model for it and then examining the model. In computational finance, however, our model is typically analysed by running computer programs, rather than solving mathematical equations. In addition to standard computational methods such as Monte-Carlo option pricing, you will also learn more advanced modelling techniques such as agent-based modelling, in which the model itself takes the form of a computer program.

The programme will provide a foundation in the core skills required for successful risk management and optimal investment by giving a grounding in the key quantitative methods used in finance, including computer programming, numerical methods, scientific computing, numerical optimisation, and an overview of the financial markets. You can then go on to study more advanced topics, including the market micro-structure of modern electronic exchanges, high-frequency finance, distributed-ledger technology and agent-based modelling.

Career prospects

Students are expected to go in to careers such as Investment Banking, Hedge Funds and Regulatory Bodies.  

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Why is this course for you?

•This course is for you if you wish to enter knowledge-led industrial sectors or to embark upon doctoral interdisciplinary study.
•This interdisciplinary programme between Mathematics, Engineering, Physics, and Astronomy gives you access to a broad range of knowledge and application in industry and academia.

What will you gain as a student?

•practical skills in computation in a range of languages and professional software
•rigorous understanding of the theory of common numerical methods
•technical knowledge in numerical modelling
•exposure to a range of common areas of application

Core Modules

Scientific Computing
Practical Programming
Computational Methods for PDEs or Finite Element Methods

Optional Modules include:

Topics in Mathematical Biology
Particle Methods in Scientific Computing
Advanced Fluid Dynamics
Data Mining and Neural Networks
Computational Fluid Dynamics
Dynamics of Mechanical Systems
Applications in Theoretical Physics

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Program overview

The ideas of applied mathematics pervade several applications in a variety of businesses and industries as well as government. Sophisticated mathematical tools are increasingly used to develop new models, modify existing ones, and analyze system performance. This includes applications of mathematics to problems in management science, biology, portfolio planning, facilities planning, control of dynamic systems, and design of composite materials. The goal is to find computable solutions to real-world problems arising from these types of situations.

The master of science degree in applied and computational mathematics provides students with the capability to apply mathematical models and methods to study various problems that arise in industry and business, with an emphasis on developing computable solutions that can be implemented. The program offers options in discrete mathematics, dynamical systems, and scientific computing. Students complete a thesis, which includes the presentation of original ideas and solutions to a specific mathematical problem. The proposal for the thesis work and the results must be presented and defended before the advisory committee.

Curriculum

Several options available for course sequence:
-Discrete mathematics option
-Dynamical systems option
-Scientific computing option

See website for individual module details.

Other entry requirements

-Submit official transcripts (in English) of all previously completed undergraduate and graduate course work.
-Submit a personal statement of educational objectives.
-Have an undergraduate cumulative GPA of 3.0 or higher.
-Submit two letters of recommendation, and complete a graduate application.
-International applicants whose primary language is not English must submit scores from the Test of English as a Foreign Language (TOEFL). A minimum score of 550 (paper-based) or 79-80 (Internet-based) is required. International English Language Testing System (IELTS) scores are accepted in place of the TOEFL exam. Minimum scores vary; however, the absolute minimum score required for unconditional acceptance is 6.5. For additional information about the IELTS, please visit http://www.ielts.org. Those who cannot take the TOEFL will be required to take the Michigan Test of English Proficiency at RIT and obtain a score of 80 or higher.
-Although Graduate Record Examination (GRE) scores are not required, submitting them may enhance a candidate's acceptance into the program.
-A student may also be granted conditional admission and be required to complete bridge courses selected from among RIT’s existing undergraduate courses, as prescribed by the student’s adviser. Until these requirements are met, the candidate is considered a nonmatriculated student. The graduate program director evaluates the student’s qualifications to determine eligibility for conditional and provisional admission.

Additional information

Student’s advisory committee:
Upon admission to the program, the student chooses an adviser and forms an advisory committee. This committee oversees the academic aspects of the student’s program, including the selection of a concentration and appropriate courses to fulfill the program’s requirements.

Cooperative education:
Cooperative education enables students to alternate periods of study on campus with periods of full-time, paid professional employment. Students may pursue a co-op position after their first semester. Co-op is optional for this program.

Part-time study:
The program is ideal for practicing professionals who are interested in applying mathematical methods in their work and enhancing their career options. Most courses are scheduled in the late afternoon or early evening. The program may normally be completed in two years of part-time study.

Nonmatriculated students:
A student with a bachelor’s degree from an approved undergraduate institution, and with the background necessary for specific courses, may take graduate courses as a nonmatriculated student with the permission of the graduate program director and the course instructor. Courses taken for credit may be applied toward the master’s degree if the student is formally admitted to the program at a later date. However, the number of credit hours that may be transferred into the program from courses taken at RIT is limited for nonmatriculated students.

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About the course

This course runs in Germany.

This course covers a range of essential topics related to distributed computing systems. Yet these modules are not isolated; each one takes its place in the field in relation to others.

The emphasis in the course is to build the connections between topics, enabling software engineers to achieve co-operation between distinct autonomous systems under constraints of cost and performance requirements.

The course is suitable for:

Recent graduates in Electrical or Electronic Engineering or Computer Science, who wish to develop their skills in the field of distributed computing systems.
Practicing engineers and computer professionals who wish to develop their knowledge in this area.
People with suitable mathematical, scientific or other engineering qualifications, usually with some relevant experience, who wish to enter this field.

Aims

The past few years have witnessed that Grid computing is evolving as a promising large-scale distributed computing infrastructure for scientists and engineers around the world to share various resources on the Internet including computers, software, data, instruments.

Many countries around the world have invested heavily on the development of the Grid computing infrastructure. Many IT companies have been actively involved in Grid development. Grid computing has been applied in a variety of areas such as particle physics, bio-informatics, finance, social science and manufacturing. The IT industry has seen the Grid computing infrastructure as the next generation of the Internet.

The aim of the programme is to equip high quality and ambitious graduates with the necessary advanced technical and professional skills for an enhanced career either in industry or leading edge research in the area of distributed computing systems.

Specifically, the main objectives of the programme are:

To critically appraise advanced technologies for developing distributed systems;
To practically examine the development of large scale distributed systems;
To critically investigate the problems and pitfalls of distributed systems in business, commerce, and industry.

Course Content

Compulsory Modules:

Computer Networks
Network Security and Encryption
Distributed Systems Architecture
Project and Personal Management
High Performance Computing and Big Data
Software Engineering
Embedded Systems Engineering
Intelligent Systems
Dissertation

Special Features

Electronic and Computer Engineering is one of the largest disciplines in the University, with a portfolio of research contracts totalling £7.5 million, and has strong links with industry.

The laboratories are well equipped with an excellent range of facilities to support the research work and courses. We have comprehensive computing resources in addition to those offered centrally by the University. The discipline is particularly fortunate in having extensive gifts of software and hardware to enable it to undertake far-reaching design projects.

We have a wide range of research groups, each with a complement of academics and research staff and students. The groups are:

Media Communications
Wireless Networks and Communications
Power Systems
Electronic Systems
Sensors and Instrumentation.

Women in Engineering and Computing Programme

Brunel’s Women in Engineering and Computing mentoring scheme provides our female students with invaluable help and support from their industry mentors.

Accreditation

Distributed Computing Systems Engineering is accredited by the Institution of Engineering and Technology (IET).

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