[DMANET] PhD position in Optimisation and Learning at the University of Liverpool

We are looking for a PhD student to develop new tools for Crystal Structure Prediction (CSP)
using combinatorial optimisation, machine learning as well as fundamental research
about this problem. Many materials critical for reaching net-zero and sustainability goals,
such as batteries, superconductors, and thermoelectrics, are crystalline. However, their
discovery remains a significant challenge. CSP is the cornerstone problem in computational
material discovery that can be leveraged to overcome this challenge.

We welcome applicants from diverse backgrounds and no prior experience in this area is
expected. Current PhD students in the group have degrees in Mathematics, Physics and
Computer Science. The successful candidate will have the opportunity to pursue various
research directions, such as exploration of combinatorial optimization techniques, design
of geometric deep learning models in the context of generative models and reinforcement
learning, and fundamental research on lattices and tilings. The project is funded by the
Leverhulme Centre for Functional Material Design – an interdisciplinary collaboration where
people with backgrounds in robotics, computer science, mathematics, physics, and chemistry
work together to overcome global challenges through the development of new transformative
materials. The studentship includes a generous research budget.

Further information and how to apply: https://www.findaphd.com/phds/project/combinatorial-optimisation-and-machine-learning-for-crystal-structure-prediction/?p158554
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Vladimir Gusev
Lecturer (Assistant Professor)
Department of Computer Science
University of Liverpool
https://www.vlgusev.co.uk/
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