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experts to acquire bespoke training and testing data; develop prototype solutions informed by the latest ideas in medical imaging AI, computer vision and robotic guidance; and evaluate models in simulated
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• Uncertainty quantification around LLMs • Constrained optimal experimental design (active learning) • Combining models and combining data / Realistic simulation of clinical trials • Developing
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that requires accurate sub-grid models (e.g., Particle-in-Cell or Vlasov codes) coupled to a hydrodynamic simulation. In general, charged-particle transport is a non-trivial task, not only because of the large
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) information-theoretic active learning, and c) capturing uncertainty in deep learning models (including large language models). The successful postholder will hold or be close to the completion of a PhD/DPhil in
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used in our work centre around optical imaging and spectroscopy and nanofabrication. The work also relies on theory and simulation, specifically focusing on numerical mean-field electrostatics
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, defensive mechanisms and related topics to the safe deployment of systems contain multiple LLM and VLM powered models. You will be responsible for Developing and implementing; capability evaluations, attacks
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Professor Chris Russell. This is an exciting opportunity for you to work at the cutting edge of AI, contributing to a major shift in how we understand and apply foundation models. The position is full-time
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on evaluating the abilities of large language models (LLMs) of replicating results from the arXiv.org repository across computational sciences and engineering. You should have a PhD/DPhil (or be near completion
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trapping or fluorescence microscopy to study DNA replication; • develop and employ simulations and data analysis routines to analyze your data; • develop an interdisciplinary skillset by
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experimental design (active learning) • Combining models and combining data / Realistic simulation of clinical trials • Developing LLMs to utilise ODEs and ProbML as tools; Code synthesis