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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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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
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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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, and enabling data-driven improvements in patient care. You will have opportunities to apply foundation models—including large language models (LLMs) to real-world clinical data. You will work with well
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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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Claudia Monaco’s research group at the Kennedy Institute of Rheumatology. In this role, you will apply single cell biology and cell signalling techniques combined with in vivo and in vitro models
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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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systems modelling including technical knowledge (e.g., in data science, input-output modelling, applied economic modelling, environmental and ecological assessments, GIS, comparative risk assessments), as