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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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About the Role We are seeking an enthusiastic and motivated postdoctoral researcher to apply advanced data analytics and machine learning techniques to real-world clinical data in the field of viral
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) in machine learning or a closely related field you should possess sufficient specialist knowledge in the discipline to work within established research programmes and have an ability to manage own
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Goldberg and will contribute to an ongoing EPSRC-funded project titled Optimisation for Game Theory and Machine Learning. The research associate will be based at Oxford and will work with Prof. Goldberg and
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Oxford’s Department of Orthopaedics (NDORMS) as well as collaborators in Bristol and Cardiff. You should have a PhD/DPhil (or be near completion) in robotics, computer vision, machine learning or a closely
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together with relevant experience. You will have a strong technical background in machine learning, especially RL and LLMs. An ability to work independently and as part of a collaborative research team is
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broad areas of relevance to the research project and partner institutions. Selection criteria: Hold, or be near to completion of, a PhD/DPhil in bioengineering, materials science, nanotechnology
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information and advice on best-practice methodologies in machine learning/deep learning. It is essential that you hold a PhD/DPhil (or close to completion) in a relevant quantitative field (e.g. biostatistics
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methods suitable for legged systems in physically-realistic simulated environments and on real robots. You should hold or be close to completion of a PhD/DPhil in robotics, computer science, machine
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on and defensive mechanisms for safe multi-agent systems, powered by LLM and VLM models. Candidates should possess a PhD (or be near completion) in Machine Learning or a highly related discispline. You