About the Project
A fully funded PhD opportunity to participate in the world-leading research undertaken by the EPSRC Doctoral Landscape Award at the University of Sheffield.
Supervised by Dr Wei Xing and Prof Jeremy Oakley.
The University of Sheffield is one of the leading Russell Group universities in the UK. We carry out cutting-edge research with strong links to industry. When you enrol to do a PhD with us, you will be working with world-leading academics and have access to top of the range facilities. As a PhD student you will have the opportunity to gain skills not only to conduct research, but also to take your career to the next level, whether you want to stay in academia, go into industry or the public sector, or set up your own company. You will have access to a range of training and support services to help you excel in your studies and beyond.
Project Description
For fifty years, computer chips improved by packing more components onto one piece of silicon. That route is closing. Systems are now built by combining several smaller pieces of silicon, each made in the technology that suits it best, inside a single package. This approach powers data centres, electric vehicles, medical scanners and phones, and it is a stated priority of the United Kingdom's semiconductor strategy.
It also creates a hard design problem. The decisions that remain manual sit above the steps software already handles: how to split a system into pieces, how to deliver power across them, how to lay them out so heat escapes, and how to judge whether an assembled system will work. Each choice trades speed, power, temperature, cost and reliability against the others. To judge a candidate design, engineers run simulations that take hours or days. With a few dozen simulations affordable and an astronomically large number of possible designs, most of the space is never examined.
Experienced engineers cope by knowing where to look. Consider a small company designing its first power management system for an electric vehicle. A veteran who has built twenty similar systems carries expectations about what tends to work, and spends the simulation budget accordingly. A newcomer has none, and this is a main barrier to entry in advanced electronics.
This project will develop artificial intelligence that builds those expectations from data rather than from a career. The idea is to learn from a large collection of past designs and simulations of related systems, and to distil that experience into an informed starting point for a system nobody has seen before. Crucially, the method must be honest about how much it knows. A confident but wrong expectation is worse than none at all, because it sends the search in the wrong direction and hides the failure until manufacture.
The student will work on three linked questions. First, how to compress experience from many past design tasks into a starting point that reports realistic uncertainty on a new task. Second, how to decide which design to simulate next and how accurately, in a way that stays safe when the learned expectations mislead. Third, how to notice from a handful of results that the expectations are wrong, and correct course.
Industrial data on these decisions is largely unavailable, so the project uses open reference designs and simulation data generated in-house, including open design patterns being produced by the national centre for multi die system design at Sheffield. A method is accepted only if it holds up on test problems chosen to be unlike those it learned from, since that is what a real new design presents. The final year connects the components to a working design procedure and measures how much simulation effort they save compared with current practice.
The project sits between statistics, machine learning and electronic engineering, and the student will be trained in all three. The supervisory team combines statistical expertise in uncertainty and decision making with an active research programme in machine learning for chip design, and the student can engage with the national centre for multi-die system design at Sheffield, which brings contact with the companies facing these problems.
The underlying question, how to reuse experience when data is scarce and experiments are expensive, recurs across engineering, drug discovery and climate modelling, so the skills gained are portable.
No prior knowledge of chip design is required. The project suits a mathematically minded graduate in mathematics, statistics, computer science, physics or engineering.
How to apply
Interested candidates are strongly encouraged to contact the project supervisors to discuss your interest in and suitability for the project prior to submitting your application.
Please refer to the EPSRC DLA webpage for detailed information about the EPSRC DLA and how to apply.
Applicants must ensure that they use the correct application form linked to the EPSRC DLA webpage and must submit an application for October 2027 entry. The form is unique to the EPSRC DLA. It contains the list of advertised projects from which to select, and three questions specific to the DLA.
The application deadline is 17:00 GMT 13 January 2027.
The majority of our DLA studentships will be allocated to candidates who qualify for Home fee status, in accordance with EPSRC funding regulations which place strict limits on international recruitment.
Funding Notes
The award will fund the full tuition fee and a maintenance stipend at the UKRI rate (currently £21,805 per annum) for 3.5 years, as well as a research grant to support costs associated with the project.
