HetSys' training programme is 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:
- Span disciplinary barriers. The most challenging real-world heterogeneous systems are intrinsically multidisciplinary, requiring integration of a diverse range of modelling methods.
- Incorporate uncertainty in modelling. Training in uncertainty quantification will enable students not only to perform simulations, but also to quantitatively assess their reliability.
- Promote robust Research Software Engineering (RSE). Training in sustainable software development will enhance software usability and extend its lifetime.
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.
During the first 18 months of the programme each student will study both core modules and optional modules, participate in a group software development project supported by academics and RSEs, and carry out an independent research project in the area of their PhD project .
The individual project also leads to a peer-to-peer activity in the second year. 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.
Our training programme will include the following topics:
Module 1 will provide an introduction to atomistic modelling techniques including Density Functional Theory, classical force field methods and an appreciation of how they interact with other modelling frameworks. Multiscale Modelling case studies by guest lecturers will show how problems involving heterogeneous systems are tackled at multiple length & time scales.
Module 2 will provide a firm grounding in macroscopic and multiscale modelling techniques, 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.
Module 3 will comprise bespoke Software Carpentry training developed by our RSE group and will ensure students understand the core principles of programming and software development. This training will position students to participate in group projects and follow advanced computing modules.
Module 4 will give an introduction to predictive modelling techniques including statistics, machine learning, data analytics and data mining, essential for solving problems in the interdisciplinary area of predictive modelling. Students will learn how to quantify uncertainty in a range of modelling approaches. Particular emphasis will be given to scalable approaches to UQ and propagation in multiscale models.
A group project supervised by a combination of academics and RSEs, will be undertaken collectively by the cohort, with students designing, specifying, optimising and implementing a small-scale simulation package to model heterogeneous systems.
Year 1 students will also complete a 12-week individual research project on a subject approved by the CDT that demonstrates detailed knowledge and approaches the frontiers of research. Typically, the project will be preparation for PhD work, but students have the option to change topic and/or supervisor after the first year.
A Peer-to-peer Project Evaluation exercise will build 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.
Optional modules will allow students to develop necessary theoretical background for their PhD projects, and gain hands-on experience with algorithms and software packages in their field.