Skip to main content Skip to navigation

HetSys Training

The HetSys' training programme is designed to enable students to become high-quality computational scientists who are comfortable working in interdisciplinary environments, have excellent communication skills, and well prepared for a wide range of future careers in areas where there is demonstrable need.

The HetSys training programme will meet three key training needs:


Overview of Training Programme

CDT training will run throughout the four year PhD programme as illustrated below, with all aspects designed to meet the three key training needs above, and to develop transferable skills. Students will be recruited directly onto projects and will have a ​supervisor from the start of their course, as well as a ​second supervisor ​in a related area and a ​cohort mentor for academic and pastoral advice. In projects with industry links there will often also be an industry co-supervisor.

HetSys Training Overview

During the first 18 months of the programme each student will study ​4 core modules (PX911, PX912, PX913 ​and PX914​) and at least 2 optional modules, participate in a ​group software development project (​PX915​) supported by academics and RSEs, and carry out an independent research project (Individual Project) ​in the area of their PhD project ​assessed through a written report and viva 12 months into the programme.

The Individual Project also leads to a peer-to-peer activity in the second year (also part of ​PX915​). These activities will contribute to the formal award of a ​postgraduate diploma ​(120 credits), which must be successfully completed 18 months into the programme. Each student will also have the opportunity to participate in the formal ​transferable skills course run by the University, which leads to a PG Certificate in Transferable Skills after 3 years.

In Years 2-4 the majority of the students’ time will be spent conducting PhD research. There will be ample opportunities for peer-to-peer learning and knowledge exchange through cohort-wide activities.

HetSys Core Modules

PX911: Multiscale modelling methods and applications I 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.
PX912: Multiscale modelling methods and applications II Providea 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.
PX913: Introduction to Scientific Software Development 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.
PX914: Predictive Modelling and Uncertainty Quantification 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.
PX915 ​Part I: Software engineering ​group project​

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 Warwick 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.

PX915 Part II: ​Peer-to-peer Project Evaluation ​(Year 2)

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 Warwick Modules Catalogue and search using the module code.

HetSys Optional Modules - updated for 2024/25

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

PX917 C​omputational Plasma Physics (15 credits)
PX918* Electronic Structure Theory for Experiment and Models (10 credits)

Quantum Chemistry

(10 credits)
PX920 Micromechanics of Materials (10 credits)
PX921 M​icro & Nano Flows across scales (10 credits)
PX923* Biomolecular Simulation (10 credits)
PX925 High Performance Computing (10 credits)
ES98E Scientific Machine Learning (10 credits)
IL939 Public Engagement (15 credits)

Modules running in the 2023/24 academic year from the above list are as follows: PX917, PX925, ES98E, IL939

* These modules (PX918, PX919 and PX923) can be expected to run in 2024/25.

HetSys students will also have access to a range of other relevant postgraduate modules from across Warwick, for example:

MA934 Numerical Methods and Algorithms

(15 credits)


Foundations of Data Analytics

(15 credits)
ES440 Computational Fluid Dynamics (15 credits)
PX449 Kinetic Theory (10 credits)