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research directions include: Reversible material representation methods for accelerated inverse design Large language, diffusion & graph neural models for materials discovery Fine tuning and architecture
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-qualification experience at the time of application; (b) strong background in SLA theories and methodologies; (c) proficiency in quantitative and qualitative research methods; (d) demonstrated ability
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representation methods for accelerated inverse design Large language, diffusion & graph neural models for materials discovery Fine tuning and architecture optimisation of foundation models Inverse design of next
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: Condensed Matter Physics / Condensed Matter Physics, Electronic Structure, Strongly Correlated Materials , Condensed Matter Theory , condensed matter theory; ultracold quantum gases , Strongly Correlated
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or an equivalent qualification. For all posts, applicants should also: (a) have solid experience in electric machines, finite element methods and theory of electromagnetic files; and (b) be able to complete