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.
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.
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.
Further information on modules and degree structure is available on the department website: Mathematical Modelling MSc
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.
Recent career destinations for this degree
Finance, actuarial and accountancy professionals are constantly in demand for their 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, one recent graduate has progressed to a mathematical modelling role at a leading transportation planning consultancy; another became a graduate trainee at 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.
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.
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.
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: Mathematics
82% 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.
MathMods is a 2-year Joint MSc programme which can be taken in 5 EU universities: University of L’Aquila in Italy (UAQ), Vienna University of Technology in Austria (TUW), Autonomous University of Barcelona in Spain (UAB), Hamburg University of Technology (TUHH) & University of Hamburg in Germany (UHH), and University of Nice - Sophia Antipolis in France (UNS).
What makes MathMods so special is its peculiar mobility scheme, that is the fact that our students will be spending their postgrad years in two or even three different European countries. You'll be indeed studying in central Italy for your first semester, then move to Austria or Germany for the second term, and finally move again to 1 of our 5 partners for your second year, based on the mobility path you'll be assigned.
Upon graduation students will be awarded a Joint Master's degree (or double, depending on where they spend their Year2).
Since its establishement in 2008 MathMods was funded by the EU Commission firstly through the Erasmus Mundus programme action 1 A (project no. 2008-0100), and later through the Erasmus+ Key Action 1 programme, project no. 2013-0227. We're currently applying for the Erasmus+ Call for Proposals 2018 to continue awarding Erasmus Munuds scholarships to our future generations. No matter the outcome, MathMods will still be running with the aid of Consortium grants and other local grants. Visit the sections Apply and Program Structure to learn more.
Study Subject(s): Semester 1 focuses on Theory, Semester2 on Numerics. Then for Year2 each partner institution offers a specific curriculum or study path:
- Mathematical models in social sciences (UAQ, Italy)
- Mathematical modelling and optimisation (UAQ, Italy)
- Stochastic modelling and optimization (UAB, Spain)
- Modelling and simulation of complex systems (TUHH or UHH, Germany)
- Mathematical modelling applications to finance (UNS, France)
- Advanced modelling and numerics for applied PDEs (TUW, Austria)
Semester4 is dedicated to thesis work
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.
Mathematical models are fundamental to how we understand, analyse and design transportation systems, but these models face challenges from the rapidly changing nature of mobility.
Innovative technologies are being harnessed to deliver new approaches to transport services, and huge volumes of data create new opportunities to examine how patterns of movement are evolving.
If you’re a highly numerate graduate with a desire to apply your quantitative 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.
97% of our graduates find employment in a professional or managerial role, or continue with further studies.*
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 analyse and improve the performance of transportation networks and flows:
And experience what it is like to be part of a project team working across disciplinary boundaries within the transport sector. Through this, gain insights into how modelling, environmental science, planning, economics and engineering can work together to develop sustainable solutions to global challenges. This industry-inspired approach will enable you to apply your knowledge to real-world 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.
With a strong focus on industry needs, our degrees will prepare you for employment in your chosen field. They will also address the multi-disciplinary nature of transport – enabling you to make effective decisions for clients, employers and society.
Other Study Options
This programme is available part time, allowing you to combine study with other commitments. You can work to fund your studies, or gain a new qualification without giving up an existing job. We aim to be flexible in helping you to put together a part-time course structure that meets your academic goals while recognising the constraints on your study time.