MSc in Mathematical Modelling and Scientific Computing Handbook (2025-26 Entry)

2. The M.Sc. Course: Content and Structure

2.1 Overview


The Master of Science in Mathematical Modelling and Scientific Computing is a 12 month course. The relevant QAA subject benchmark statement is Mathematics, Statistics and Operational Research.

2.2 Aims

The aims of the programme are as described below.

  1. To provide graduates with a strong mathematical background with the skills necessary to apply their expertise to the solution of real problems.
  2. To provide students with a systematic understanding of core areas in both applied mathematics and numerical analysis, as well as advanced topics in one or both of these areas.
  3. To lay the foundation for further research for a career as a research mathematician in a whole range of application areas.
  4. To develop students’ skills so that they are able to:
    • formulate a well posed problem from a possibly sketchy verbal description;
    • carry out relevant mathematical analysis;
    • develop an appropriate numerical scheme;
    • present and interpret these results.
    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.
2.3 Intended Learning Outcomes

Students on the course will gain a knowledge of:

  • core methods of applied mathematics and numerical analysis;
  • computer coding in Python;
  • mathematical modelling;
  • more advanced topics in modelling, methods and numerical analysis;
  • how to undertake a short research project in an area of applied mathematics and/or numerical analysis;
  •  how to communicate mathematics effectively both orally (in conversation and by giving presentations) and in written form.
2.4 Course Structure

During the course you will be assessed on 12 units counted as follows:

  • Four core courses on mathematical methods and numerical analysis (1 unit each)
  • Two special topics chosen from a range of about 20 courses (1 unit each)
  • Two case studies: one in each of mathematical modelling and scientific computing (1 unit each)
  • A dissertation and viva voce examination (4 units)

More details of these units are given below.

You will be assigned a supervisor on arrival in Oxford whose main role throughout the first two terms is to act as an academic advisor. They will be able to help with decisions about which options to take and the Course Director is also available for advice.

2.4.1 Core Courses

There are four core courses with a weighting of 1 unit each:

  • A1: Mathematical Methods I
  • A2: Mathematical Methods II
  • B1: Numerical Linear Algebra and Numerical Solution of Partial Differential Equations
  • B2: Further Numerical Linear Algebra and Continuous Optimisation

A1 and B1 are taken during Michaelmas Term and are examined during Week 0 of Hilary Term. A2 and B2 are taken during Hilary Term and are examined during Week 0 of Trinity Term. The examinations will be sat in person and so students are expected to be present in Oxford for these.

Each core course consists of 24 lectures. The lectures are backed up by one or two problem solving classes per week, usually with no more than 15 students per class, in which the class tutor goes through the problems given out in lectures as well as clarifying any of the material as necessary. However, the course is assessed solely by the examination. 

Revision classes will be organised before the written examinations and students are encouraged to look at and attempt past examination papers  available online at https://courses.maths.ox.ac.uk/mod/folder/view.php?id=65499.

Note that calculators will not be allowed, or required, in the written examinations.

Details of the synopses for the core courses are available online at the MMSC Moodle page.


2.4.2 Special Topics

You must complete two special topics, with each special topic having a weighting of 1 unit. Special topic courses usually consist of 16 lectures. There is a great variety of special topic lecture courses which are classified under the broad headings of Modelling/Methods, [M], and Computation, [C]. You should complete one Modelling/Methods course and one Computation course. A special topic is usually assessed by a mini-project on a topic agreed with the lecturer. If you wish to do a special topic on one of these courses you should discuss a suitable plan with the lecturer by the end of term, and submit a pdf of your topic to the online site by the deadline listed in the Diary of Important Events. Usually special topics based on Michaelmas Term lecture courses should be submitted by the deadline at the beginning of Hilary Term. The exception to this is if you wish to submit two special topics based on Michaelmas Term courses. In this case, one special topic should be submitted by the deadline at the beginning of Hilary Term and the second special topic may be submitted by the deadline at the beginning of Trinity Term. However, if you wish to submit two special topics based on Hilary Term courses, these must both be submitted by the deadline at the beginning of Trinity Term.

Special topic marks are awarded by the examiners on the recommendation of the assessors; usually the relevant course lecturer and a second independent marker. Once the official marks have been released, you will also be sent the feedback provided by the assessors.

The special topic guidelines are given in Appendix A.

These are the special topic courses expected to be available for the Academic year 2025-26.

Michaelmas Term
  • Elasticity and Plasticity [M]
  • Further Mathematical Biology [M]
  • Integer Programming [C]
  • Machine Learning [C]
  • Mathematical Geoscience [M]
  • Mathematical Mechanical Biology [M]
  • Mathematical Physiology [M]
  • Perturbation Methods [M]
  • Theories of Deep Learning [C]
  • Topics in Fluid Mechanics [M]
  • Viscous Flow [M]
Hilary Term
  • Applied Complex Variables [M]
  • Computational Algebraic Topology [C]
  • Finite Element Methods for PDEs [C]
  • Mathematical Models of Financial Derivatives [M]
  • Networks [M]
  • Optimal Control [M]
  • Optimisation for Data Science [C]
  • Solid Mechanics [M]
  • Stochastic Modelling of Biological Processes [M]
  • Waves and Compressible Flow [M]

Details of the synopses for the special topic courses are available online at the MMSC Moodle page.

2.4.3 Case Studies

Some of the time in the induction week will be spent teaching Python, and hopefully this will provide a good introduction if you do not already know the language, and revision if you do. In MT you will take the course Practical Numerical Analysis (1 lecture per week throughout term) in which you will use Python to investigate numerical algorithms as described in lectures. You will also attend Mathematical Modelling classes (3 hours per week in weeks 5–8 of term) which will also include group work and presentation of results.

The skills learnt in these courses are further developed in HT when you participate in the Case Studies in Scientific Computing and in Mathematical Modelling. Your group project will be individually written up for assessment for each course. These assessments are worth 1 unit each. The Case Studies in Scientific Computing consist of developing numerical solutions to problems of interest, possibly using algorithms beyond the scope of the lecture courses. You will work in groups of 4 or 5 and meet with the course lecturer weekly over four weeks to report on progress and discuss future directions. The course is then assessed by an individual written report. The Case Studies in Mathematical Modelling extend the MT course and you will work in groups to model problems of practical interest. Each group meets with the group leader weekly and at the end of the term they give a presentation; the mark for the presentation makes up 20% of the final mark for this unit. The remaining 80% of the mark is for your individual written report.

2.4.4 Dissertation

You will prepare your dissertation during Trinity Term and the long vacation. Your dissertation topic should be selected in consultation with your supervisor and the details of the form and scope of the dissertation are described in the Regulations. There is a list of possible dissertation projects on the course dissertation page, although note that this list will not be updated for the 2025–26 academic year until February 2026. 

The topics suitable for dissertations will be presented by the appropriate supervisors at a meeting in February. Also you are encouraged to talk to any potential supervisors, which includes most academics or researchers in OCIAM or the Numerical Analysis Group. Note that the supervisor allocated to you in the first term will not usually turn out to be the supervisor for your dissertation.

You will be required to give a short talk and answer questions on the background to your dissertation topic at an open meeting, attended by supervisors, examiners and and other students, to be held in early June. The main body of the final dissertation (excluding appendices etc.) should usually be 40–50 pages in length (less than 55 pages without penalty). Precise guidelines on the length of the dissertation, the formatting and the penalties for overlong submissions are available in the dissertation handbook which can be downloaded from the MMSC course webpage.

You should submit a pdf version of your dissertation by 12 noon on Wednesday 2nd September 2026.

Your dissertation will be read by two internal examiners, neither of whom will be your supervisor. The oral examination (viva) will be held in mid-September and you will be expected to answer questions on your dissertation. Each viva will last approximately 30 minutes and sub fusc should be worn. At least two examiners will attend the viva and ask questions. They will begin by asking you to summarise briefly the main contributions of your dissertation and you are advised to prepare a few slides for this. The final mark for your dissertation and viva will be decided after the viva by the examiners who have read your work and were present in the viva.