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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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and synthesis of new materials. You should have a PhD in a relevant field (Computer Science, Mathematics are most likely to fit the role, but we are open to Chemistry, Materials Science, Chemical
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PhD in a relevant field (Computer Science, Mathematics are most likely to fit the role, but we are open to Chemistry, Materials Science, Chemical Engineering, etc.), expertise in cutting-edge AI and
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PhD in a relevant field (Computer Science, Mathematics are most likely to fit the role, but we are open to Chemistry, Materials Science, Chemical Engineering, etc.), expertise in cutting-edge AI and
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Required Department Minimum Qualifications: Ph.D. in computer science, medical, health informatics, or other related field. Candidates will need to have completed their PhD or have it completed by the start
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: PhD in Materials Science, Chemistry, Physics, Computer Science, or a related field. Strong expertise in machine learning for materials science (e.g., generative models, neural networks, active learning
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. Qualifications/Requirements Qualifications / Discipline: - PhD from a reputable institution in Physics, Bio-imaging, Computer Science, or a scientific domain closely related to Machine Learning. - The candidate
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of neural circuitry across rodent species to bridge this knowledge-gap. Position Requirements Candidates must hold a PhD degree (or equivalent) in neuroscience, biomedical engineering or a related field. The
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role The role involves undertaking high quality research as part of an exciting new internationally funded research project investigating the neural and computational basis of anergia and effort
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at national and international conferences. The project is based around constructing physics-informed neural networks (PINNs) to model the fluid dynamics of Earth’s core where the global magnetic field is