Overview
Semiconductor fabrication is one of the most complex and precision‑driven forms of manufacturing. At nanometre scales, even subtle variations in process conditions can introduce defects that degrade device performance, reduce yield, and drive up production costs. Addressing this challenge requires new modelling approaches that can capture the full complexity of fabrication processes and enable optimisation before physical manufacturing begins.
This project aims to develop advanced deep learning models capable of predicting fabrication outcomes and guiding fabrication recipe optimisation. By learning directly from experimental and process data, these models will enable a shift from iterative, trial‑and‑error fabrication towards predictive and data‑driven manufacturing.
Role and Responsibilities
We are seeking a highly motivated Machine Learning Researcher to join a multidisciplinary team of fabrication engineers and AI specialists at the University of Southampton, within the School of Electronics and Computer Science, working in the group of Dr Yasir Noori.
In this role, you will work at the interface of machine learning and semiconductor engineering, developing models that predict post‑fabrication device characteristics from process parameters. You will engage with complex, high‑dimensional datasets derived from real fabrication workflows, including microscopy, spectroscopy, and electrical performance measurements. You will work closely with fabrication engineers to translate physical processes into machine learning models, design and train deep learning architectures, and evaluate their ability to generalise across different process conditions. The models you develop will not remain confined to the research lab, but will be validated experimentally and tested at an industrial scale in collaboration with global companies in semiconductor fabrication and electronic design automation.
The position offers a rare opportunity to apply machine learning to an important technical challenge with substantial potential impact. You will also be involved in supervising PhD students and junior researchers and play a central role in shaping the research direction of the team. Your work is also expected to contribute to the development of innovative technologies with a clear pathway to commercialisation through the spin‑out company Deep Fabrication, to influence how semiconductor manufacturing is approached in practice.
Additional Highlights
The role will provide you with deep exposure to nanofabrication processes, experience working with industry‑relevant datasets and problems, and the opportunity to publish in leading journals and conferences. It is particularly well‑suited to candidates who are motivated by applying machine learning to real‑world systems where the underlying physics is complex and not fully understood.
Duration
This position is offered for 24 months in the first instance, with the possibility of extension for a further 12 months.
EEO Statement
We are committed to equality, diversity and inclusion and welcome applicants who support our mission of inclusivity.
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