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.
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.
Options include a wide selection of modules across UCL Engineering and UCL Mathematical & Physical Sciences.
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
For a comprehensive list of the funding opportunities available at UCL, including funding relevant to your nationality, please visit the Scholarships and Funding website.
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.
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.
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.
The course provides you with a strong mathematical background with the skills necessary to apply your expertise to the solution of real finance problems. You will develop skills so that you are able to formulate a well posed problem from a description in financial language, carry out relevant mathematical analysis, develop and implement an appropriate numerical scheme and present and interpret these results.
The course lays the foundation for further research in academia or for a career as a quantitative analyst in a financial or other institution.
You will take three introductory courses in the first week. The introductory courses cover partial differential equations, probability and statistics and MATLAB.
The first term focuses on compulsory core material, offering 80 hours of lectures and 40 hours of classes/practical. The core courses are as follows:
In the second term, three streams are offered; each stream consists of 32 hours of lectures and 16 hours of classes/practical. The Tools stream is mandatory and you will also take either the Modelling stream or the Data-driven stream.
As well as the streams, the course includes a compulsory one-week (24 hours of lectures) intensive module on quantitative risk management which is to be held in/around the week before the third term.
The third term is dedicated to a dissertation project which is to be written on a topic chosen in consultation with your supervisor.
The second component of the financial computing course, Financial Computing with C++ 2 (24 hours of lectures and practicals in total), is held shortly after the third term.
The examination will consist of the following elements:
MSc graduates have been recruited by prominent investment banks and hedge funds. Many past students have also progressed to PhD-level studies at leading universities in Europe and elsewhere.
Students will gain deep knowledge and skills in cutting-edge computational techniques for real world science and engineering applications to meet industry demand.
The Applied Computational Science and Engineering MSc will educate future domain-specialists in computational science. This course will expand your knowledge of numerical methods, computational science, and how to solve large scale problems by applying novel science and engineering approaches. It is suitable for graduates of disciplines including mathematics and physical sciences, geophysics and engineering, and computer science.
Students will have the chance to participate in individual and group research projects as well as to write reports and present technical work, developing the project management and numerical skills that are desired by employers.
The study programme consists of eight taught modules, and one individual research project which accounts for one third of the study programme.
Modern programming methods
Modelling dynamical processes
Applying computational science
Patterns for parallel programming
Inversion and optimisation
Term 3 (summer)
This immersive, hands-on MSc course will enable students to develop their skills and techniques for a range of science and engineering applications utilising High Performance Computing resources. Students will learn alongside world-class researchers in the Department of Earth Science and Engineering. There will be a strong emphasis on high productivity problem solving using modern computational methods and technologies, including computer code development and parallel algorithms.
Applicants who want to pursue analytical careers in industry geoscience and engineering are a target for this course. Graduates will develop the skills necessary to enter the modern industrial workforce. This MSc will also prepare for your PhD studies in fields such as computational techniques, simulation, numerical modelling, optimisation and inversion, heat transfer, and machine learning applications.
The Applied Computational Science and Engineering MSc programme will ensure that students are able to apply appropriate computational techniques to understand, define and develop solutions to a range of science and engineering problems. You will have the chance to participate in individual and group research projects as well as to write reports and present technical work, developing the project management and numerical skills desired by employers.
Duration: 1 year full-time
Start Date: October 2018
Campus: South Kensington, London
ECTS: 90 Credits
Please contact Postgraduate Education Manager, Samantha Symmonds, with any queries: [email protected]
The Applied Computational Science and Engineering MSc is subject to College approval.
This one-year programme at the University of Edinburgh will immerse you in the most current developments in chemical engineering, through a combination of taught modules, workshops, a research dissertation, and a number of supporting activities delivered by the key experts in the field.
The programme will develop from fundamental topics, including modern approaches to understanding properties of the systems on a molecular scale and advanced numerical methods, to the actual processes, with a particular emphasis on energy efficiency, to the summer dissertation projects where the acquired skills in various areas are put into practice, in application to actual chemical engineering problems.
The programme develops from compulsory courses, emphasizing modern computational techniques and research methods, to a range of options. It is complemented by a strong management and economics component, culminating in a research project leading to a masters thesis.
Students must select one of the following courses during semester one:
Plus, five or six courses (depending on the weighting of the course) from the options listed below in semester two:
On completion of the research dissertation, the students will be able to:
Our graduates enjoy diverse career opportunities in oil and gas, pharmaceutical, food and drink, consumer products, banking and consulting industries. Examples of the recent employers of our graduates include BP, P&G, Mondelēz International, Doosan Babcock, Atkins, Safetec, Xodus Group, Diageo, Wood Group, GSK, Gilead Sciences, ExxonMobil, Jacobs, Halliburton, Cavendish Nuclear to name a few. This wide range of potential employers means that our graduates are exceptionally well placed to find rewarding and lucrative careers. According to the Complete University Guide, the chemical engineering programme at the University of Edinburgh is ranked one of the top in the UK in terms of graduates prospects.
Find out more about career opportunities:
The MSc in Advanced Chemical Engineering may also lead to further studies in a PhD programme. With the 94% of our research activity rated as world leading or internationally excellent (according to the most recent Research Excellence Framework 2014), Edinburgh is the UK powerhouse in Engineering. As an MSc student at Edinburgh you will be immersed in a research intensive, multidisciplinary environment and you will have plenty of opportunities to interact with PhD, MSc students and staff from other programmes, institutes and schools.
Find out more about our research:
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.
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.
Students are expected to go in to careers such as Investment Banking, Hedge Funds and Regulatory Bodies.
Visit our website for more information on fees, scholarships, postgraduate loans and other funding options to study Erasmus Mundus Computational Mechanics at Swansea University - 'Welsh University of the Year 2017' (Times and Sunday Times Good University Guide 2017).
Swansea University has gained a significant international profile as one of the key international centres for research and training in computational mechanics and engineering. As a student on the Master's course in Erasmus Mundus Computational Mechanics, you will be provided with in-depth, multidisciplinary training in the application of the finite element method and related state-of-the-art numerical and computational techniques to the solution and simulation of highly challenging problems in engineering analysis and design.
The Zienkiewicz Centre for Computational Engineering is acknowledged internationally as the leading UK centre for computational engineering research. It represents an interdisciplinary group of researchers who are active in computational or applied mechanics. It is unrivalled concentration of knowledge and expertise in this field. Many numerical techniques currently in use in commercial simulation software have originated from Swansea University.
The Erasmus Mundus MSc Computational Mechanics course is a two-year postgraduate programme run by an international consortium of four leading European Universities, namely Swansea University, Universitat Politècnica de Catalunya (Spain), École Centrale de Nantes (France) and University of Stuttgart (Germany) in cooperation with the International Centre for Numerical Methods in Engineering (CIMNE, Spain).
As a student on the Erasmus Mundus MSc Computational Mechanics course, you will gain a general knowledge of the theory of computational mechanics, including the strengths and weaknesses of the approach, appreciate the worth of undertaking a computational simulation in an industrial context, and be provided with training in the development of new software for the improved simulation of current engineering problems.
In the first year of the Erasmus Mundus MSc Computational Mechanics course, you will follow an agreed common set of core modules leading to common examinations in Swansea or Barcelona. In addition, an industrial placement will take place during this year, where you will have the opportunity to be exposed to the use of computational mechanics within an industrial context. For the second year of the Erasmus Mundus MSc Computational Mechanics, you will move to one of the other Universities, depending upon your preferred specialisation, to complete a series of taught modules and the research thesis. There will be a wide choice of specialisation areas (i.e. fluids, structures, aerospace, biomedical) by incorporating modules from the four Universities. This allows you to experience postgraduate education in more than one European institution.
Modules on the Erasmus Mundus MSc Computational Mechanics course can vary each year but you could expect to study the following core modules (together with elective modules):
Numerical Methods for Partial Differential Equations
Advanced Fluid Mechanics
Finite Element Computational Analysis
Entrepreneurship for Engineers
Finite Element in Fluids
Nonlinear Continuum Mechanics
Computational Fluid Dynamics
Dynamics and Transient Analysis
Reservoir Modelling and Simulation
The Erasmus Mundus Computational Mechanics course is accredited by the Joint Board of Moderators (JBM).
The Joint Board of Moderators (JBM) is composed of the Institution of Civil Engineers (ICE), the Institution of Structural Engineers (IStructE), the Chartered Institution of Highways and Transportation (CIHT), and the Institute of Highway Engineers (IHE).
This degree is accredited as meeting the requirements for Further Learning for a Chartered Engineer (CEng) for candidates who have already acquired an Accredited CEng (Partial) BEng(Hons) or an Accredited IEng (Full) BEng/BSc (Hons) undergraduate first degree.
See http://www.jbm.org.uk for further information.
This degree has been accredited by the JBM under licence from the UK regulator, the Engineering Council.
Accreditation is a mark of assurance that the degree meets the standards set by the Engineering Council in the UK Standard for Professional Engineering Competence (UK-SPEC). An accredited degree will provide you with some or all of the underpinning knowledge, understanding and skills for eventual registration as an Incorporated (IEng) or Chartered Engineer (CEng). Some employers recruit preferentially from accredited degrees, and an accredited degree is likely to be recognised by other countries that are signatories to international accords.
On the Erasmus Mundus MSc Computational Mechanics course, you will have the opportunity to apply your skills and knowledge in computational mechanics in an industrial context.
As a student on the Erasmus Mundus MSc Computational Mechanics course you will be placed in engineering industries, consultancies or research institutions that have an interest and expertise in computational mechanics. Typically, you will be trained by the relevant industry in the use of their in-house or commercial computational mechanics software.
You will also gain knowledge and expertise on the use of the particular range of commercial software used in the industry where you are placed.
The next decade will experience an explosive growth in the demand for accurate and reliable numerical simulation and optimisation of engineering systems.
Computational mechanics will become even more multidisciplinary than in the past and many technological tools will be, for instance, integrated to explore biological systems and submicron devices. This will have a major impact in our everyday lives.
Employment can be found in a broad range of engineering industries as this course provides the skills for the modelling, formulation, analysis and implementation of simulation tools for advanced engineering problems.
“I gained immensely from the high quality coursework, extensive research support, confluence of cultures and unforgettable friendship.”
Prabhu Muthuganeisan, MSc Computational Mechanics
The ICMA Centre’s financial engineering degree is highly respected by quantitative analysts and their employers. The credit crunch and subsequent events have emphasised the need to develop better pricing and better hedging models for all complex products. The practical and quantitative skills that you will develop on the programme will equip you to meet this challenge.
Our compulsory modules provide a firm grounding in probability theory, stochastic calculus, derivatives pricing, quantitative and numerical methods, structuring products, volatility analysis, and the modelling of credit, equity, foreign exchange and interest rate derivatives. We also provide a thorough training in C++ and other programming tools.
Optional modules will allow you to focus on risk analysis, portfolio management, designing trading strategies or econometric analysis. This newly structured degree aims to further enhance the strong reputation of its precursor – the MSc in Financial Engineering and Quantitative Analysis, which was established back in 1999. A good background in mathematics is required for acceptance to this programme (see entry requirements below).
October – December: Part 1 Autumn Term
January: Part 1 Exams
January-April: Part 2 Spring Term
May – June: Part 2 Exams
June – August (12 month programme only): Part 3
August/Sep (12 month programme only): Part 3 Coursework deadlines
Part 1 compulsory modules
Part 2 compulsory modules
Part 2 optional modules
Students on the 9-month (12-month) programme can select 40 (20) credits from the following modules:
Part 3 optional modules
Students on the 12-months programme should take 20 credits from the following:
Full-time: 9 months Full-time: 12 months
Students will be resident and undertake full-time study in the UK. Under both, the 9 and 12-month programmes students take compulsory and/or elective modules in Part 2.
The 12 month option involves taking an elective 20 credit module between July and August, which would also mean a 20 credit reduction in the number of taught modules taken in the spring term.
Many of our financial engineering graduates are now working as Quants in large London banks and other financial institutions. Others have pursued PhDs and have successful academic careers. Financial instruments are becoming ever more sophisticated, so graduates that understand complex modelling techniques are always in great demand. The high quantitative content of this programme opens many doors to a wide range of careers. You could structure and develop new debt or equity solutions to meet clients funding and hedging needs, or you could become a proprietary trader in exotic derivatives, or a software specialist or a quantitative analyst supporting the traders.
There are excellent opportunities on the buy-side, with hedge funds and investment institutions, as well as in investment banking and in software analytics. Opportunities in quantitative research, or with a rating agency, are among the many other attractive alternatives. Outside of mainstream banking and investment, you might also consider firms involved in commodity and energy trading, or the treasury divisions of leading multinationals and management consultancies.
ICMA Fixed Income Certificate
To obtain the requisite knowledge to pass the rigorous FIC exam, students are required to take the ICMA Centre Fixed Income Cash and Derivatives Markets module at Part 2. In order to receive the FIC certificate, students will need to register and pass the FIC exam through ICMA.
The Masters in Mathematics/Applied Mathematics offers courses, taught by experts, across a wide range. Mathematics is highly developed yet continually growing, providing new insights and applications. It is the medium for expressing knowledge about many physical phenomena and is concerned with patterns, systems, and structures unrestricted by any specific application, but also allows for applications across many disciplines.
Modes of delivery of the Masters in Mathematics/Applied Mathematics include lectures, laboratory classes, seminars and tutorials and allow students the opportunity to take part in project work.
If you are studying for the MSc you will take a total of 120 credits from a mixture of Level-4 Honours courses, Level-M courses and courses delivered by the Scottish Mathematical Sciences Training Centre (SMSTC).
You will take courses worth a minimum of 90 credits from Level-M courses and those delivered by the SMSTC. The remaining 30 credits may be chosen from final-year Level-H courses. The Level-M courses offered in a particular session will depend on student demand. Below are courses currently offered at these levels, but the options may vary from year to year.
The project titles are offered each year by academic staff and so change annually.
Career opportunities are diverse and varied and include academia, teaching, industry and finance.
Graduates of this programme have gone on to positions such as:
Maths Tutor at a university.
There is a growing need for qualified professionals with expertise in environmental modelling. The UCL Environmental Modelling MSc is a cross-disciplinary degree that provides rigorous technical and scientific training for the next generation of environmental modelling professionals.
You will gain a well-rounded training in the role, implementation and application of models in environmental science. Core modules provide a critical perspective on model-based science, and introduce essential computational and numerical methods. The programme is contextualised with reference to the challenges of understanding both natural and human-induced changes to a variety of environmental systems.
Students undertake modules to the value of 180 credits.
The programme consists of four core modules (60 credits), optional modules (60 credits) and a research dissertation (60 credits).
A Postgraduate Diploma (120 credits, full-time nine months, part-time two years) is offered.
A Postgraduate Certificate (60 credits, full-time 12 weeks, part-time one year) is offered.
Options may include:
Other MSc modules offered across UCL may be taken at the discretion of the MSc convenor
All students undertake an independent research project, culminating in a dissertation of approximately 12,000 words and an oral presentation.
Teaching and learning
The programme is delivered through a combination of lectures, seminars, tutorials, and laboratory and computer-based practical classes. Assessment is through independent project work, practical-based and written coursework, written examinations and the dissertation.
Further information on modules and degree structure is available on the department website: Environmental Modelling MSc
The programme has been designed to provide an ideal foundation for PhD research, or for employment with environmental monitoring and protection agencies, industry and environmental consultancies. Graduates have gone on to careers as management consultants, business analysts and university researchers.
Recent career destinations for this degree
Modelling was identified as the highest priority UK skills gap in a government review of the environmental sector. This MSc programme exposes students to the full range of environmental modelling which places graduates in a strong position to find employment. We anticipate that graduates of this MSc are either employed in the private environmental consulting sector or undertake a PhD.
Careers data is taken from the ‘Destinations of Leavers from Higher Education’ survey undertaken by HESA looking at the destinations of UK and EU students in the 2013–2015 graduating cohorts six months after graduation.
The Environmental Modelling MSc is run by UCL Geography which enjoys an outstanding international reputation for its research and teaching. Research groups contributing to the MSc include those concerned with environmental modelling and observation, past climates, and recent environmental change and biodiversity.
The programme draws on the unrivalled strengths of UCL in environment modelling. Our expertise encompasses state-of-the-art global climate models, regional ocean models, advanced hydrodynamic and hydrological simulations, palaeoclimate reconstruction over geological to recent historical timescales, earth observation-derived vegetation and carbon cycle modelling, and model-based assessment of climate change impacts on coastal, estuarine and freshwater systems.
The Research Excellence Framework, or REF, is the system for assessing the quality of research in UK higher education institutions. The 2014 REF was carried out by the UK's higher education funding bodies, and the results used to allocate research funding from 2015/16.
The following REF score was awarded to the department: Geography
81% rated 4* (‘world-leading’) or 3* (‘internationally excellent’)
Learn more about the scope of UCL's research, and browse case studies, on our Research Impact website.