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Provides an introduction to atomistic modelling techniques including DFT, classical force field methods and an appreciation of how they interact with other modelling frameworks. Students will learn how to design atomistic simulations of condensed matter or molecular systems, and how to identify simulation methodologies appropriate to bridging multiple length scales, balancing accuracy vs. cost. They will gain exposure to software packages supporting interoperability between methods, e.g. the Atomic Simulation Environment. Multiscale Modelling case studies by guest lecturers will show how problems involving heterogeneous systems are tackled at multiple length & time scales.

Provide a a firm grounding in macroscopic and multiscale modelling techniques, with lectures on foundations of continuum mechanics, thermodynamics, fluid dynamics, solid mechanics, and recent developments in multiscale fluid, plasma and solid mechanics, again with an emphasis on applications and on the links between methods and across scales. Topics covered will range from the basics of continuum mechanics and thermodynamics concepts through to demonstrating the route from underlying models via algorithms to practical implementation in simulation packages.

Comprises of bespoke Python and Fortran training developed by our RSE group and will ensure students understand the core principles of programming and software development, gain experience with writing, debugging and reading code in high- and low-level languages, and learn to use common tools for data analysis and visualization. Lectures are delivered by the Research Software Engineering group of the Scientific Computing Research Technology Platform, and will cover fundamental operation of a computer, use of version control, debugging tools, and approaches to group-based software development. Comprehensive Fortran training is provided, positioning students to follow further programming option modules, most notably PX425: High Performance Computing.

Gives an introduction to predictive modelling techniques including statistics, machine learning and data analytics essential for solving problems in the interdisciplinary area of predictive modelling.

Lectures cover an introduction to uncertainty, probability and statistics, sensitivity analysis, linear regression, uncertainty propagation using Monte Carlo sampling, Gaussian process regression, Polynomial Chaos and inverse problems. Guest lectures include topics such as probabilistic numerical methods and model formulation.

Supervised by a combination of academics and RSEs, this module will be undertaken collectively by the cohort, with students designing, specifying, optimising and implementing a small-scale simulation package to model heterogeneous systems. Examples include density functional theory, molecular dynamics, computational fluid dynamics and finite element analysis. Careful division of responsibilities and integration of work will be required. ​This will be valuable for students’ future careers, as well as making it easier to distribute software developed in their PhD research. This 25 CAT module will include 2 seminars on ​intellectual property and ​software licensing with input from º£½ÇÉçÇø Ventures, who commercialise innovations produced from research carried out in the University.

Year 1 students will also complete ​a 12-week individual research project ​which requires detailed field-specific knowledge and approaches the frontiers of research, with the innovative requirement to include explicit quantification of uncertainties and/or modern aspects of software design. In general, the project will be preparation for the main PhD work. The first year report is not for credit within the PG Diploma, which allows the work undertaken to be included in the PhD thesis, but the supervisory team must be satisfied with the progress during this time for progression to the PhD.

Building on output of individual research projects: another cohort member will be assigned to evaluate the quality of a student’s model error estimates and/or compliance with research software engineering principles, e.g. by running models with an ensemble of inputs or on a selection of architectures (another opportunity for PUE profiling). Software testers will be encouraged to discuss the process with the software authors, fostering dialogue across the cohort.

Across Years 1 and 2, students must choose at least ​2 optional modules (totalling 30+ credits) from an approved set of MSc-level modules including 7 bespoke HetSys modules: these allow students to develop necessary theoretical background for their PhD projects, and gain hands-on experience with algorithms and software packages in their field.

For further information on the modules please visit the online and search using the module code.

HetSys Optional Modules - updated for 2026/27

Students will be required to study 15 or more credits from optional modules that cover topics including: