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MRes Programme Content

All the modules of the MRes component of the STOR-i programme are compulsory. You will find them listed below.

The STOR602, STOR605, STOR606 and STOR607 modules (October-November, weeks 1-5) provide a firm grounding in important core material and are taught in a traditional lecture-workshop style.

Module STOR608 (November-December, weeks 7-10) introduces you to a range of topical areas of the Statistics and Operational Research disciplines, with the opportunity to work in teams intensively investigating these at your own pace.

Modules STOR601 (November-April, weeks 6-20) and STOR604 (February-April, weeks 15-20) offer exposure to a diverse range of optional research topics from Statistics and Operational Research. This choice allows you to specialise in subjects that particularly interest you and help you to develop and test your research interest in these topics.

The STOR601 module also provides training in a broad range of research skills, including: team working, end-to-end problem solving, dissemination skills (e.g. talks, blogs, posters), programming in a range of languages, key software engineering skills, and responsible research innovation.

Module STOR603 (June-September, weeks 27-SV9) is where you develop your research plan for your PhD on a project, either jointly with an industrial partner or with an internationally leading academic from one of our selected strategic partnerships.

Core Modules


Area of Study

During your first year as a STOR-i student you will study for an MRes covering the following areas:

Probability and Stochastic Processes

Introduction to probability, Markov processes, Poisson processes and their use for modelling. The evaluation of complex stochastic properties via simulation.


Linear programming, mixed-integer programming, heuristics for large scale problems, stochastic programming, stochastic dynamic programming.

Likelihood Inference

Model-based (likelihood) inference for generalised linear models and stochastic processes and model diagnostics, randomisation methods for non-parametric testing.

Computer Simulation

Modelling for planning and decision support, systems ideas including complexity and feedback, stochastic discrete event simulation, output analysis with model validation, computational challenges including parallelisation.

Bayesian Inference

Bayesian inference, prediction and decision making. Contrasts between Bayesian and classical statistics.

Computational Intensive Methods

Conjugate analyses, importance sampling approximations, and MCMC for analysing complex stochastic systems.

Skills for Research and Industry

Presentation skills for non-technical and technical talks/posters/web design. Computer skills including programming in R and C.

Scientific Modelling

Skills for eliciting relevant background to problems through to conceptualising these in a model formulation which integrates the relevant scientific knowledge with STOR methods which capture an appropriate level of assumption.

Industrial Problem Solving Days

A current open industrial problem will be presented to the students in groups which are facilitated by staff and current students. An outline approach or solution will be developed for presentation to the collaborator.

Topical Research

Overview Presentations on thriving research areas in STOR. Students will be expected to produce a summary and a brief literature review.

PhD Research Proposal

Literature review, preliminary study and development of a firm plan for the PhD.

Learning Methods

The scheme of study offers methods of teaching and learning that provide opportunities for students to develop independence of thought and critical judgement. Generally as the scheme progresses, teaching and learning moves from methods and approaches which include more formal staff input and directed learning, towards increasingly independent and self-directed learning (culminating in the PhD process).

All teaching and learning methods are designed in relation to programme and individual module aims, in order to provide opportunities for students to demonstrate achievement of appropriate learning outcomes.

Note that there is a strong emphasis throughout on problem-based learning involving real world problems with real end-user stakeholders and this effectively drives the rest of the programme.

Student learning comes through a wide range of approaches:

  • Individual and Group Project-based learning – provides opportunities for students to take control and manage their own learning and to demonstrate skills and competencies in areas such as research, problem-solving, and reporting.
  • Lectures – enable dissemination of a specific body of knowledge to students. Ideas and issues generated by lectures will be elaborated through class discussions, workshops, weekly coursework, project learning, group critiques, essays and reports. Guest lectures will be employed as and when appropriate including a range of industrial lectures offered by our industrial partners.
  • Group critiques involving peers and tutors – provide opportunities for the development of intellectual skills in constructing, communicating and supporting arguments within a constructive learning environment.
  • External and interdisciplinary projects – can offer opportunities for students to demonstrate knowledge and understanding of, and practical skills in, professional working practices and methodologies.
  • Seminars – provide a forum for the discussion, debate and a conversation about a range of topics, ideas and contemporary issues. They provide opportunities for the presentation and discussion of inquiry-based class projects, and offer opportunities for the interchange of opinions, views, knowledge and experiences.
  • Formal presentations – reflect professional practice and provide opportunities for the development of transferable communication skills together with intellectual skills, such as critical analysis, prioritising information and arguments, and evaluation.
  • Project reports – provide opportunities for students to demonstrate competencies in research techniques, critical evaluation, design skills and transferable skills.
  • Extended projects and dissertations – provide opportunities for students to demonstrate effective self-managed learning and a broad range of competencies from technical skills and research/enquiry through to independence of thought, critical analysis, creative thinking, design skills, presentation and visualisation abilities and written communication.