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developing cutting-edge active-learning (Bayesian optimisation) methods that integrate chemical knowledge by capitalising on Large Language Models (LLMs) as well as human knowledge. You should have a PhD in
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developing cutting-edge active-learning (Bayesian optimisation) methods that integrate chemical knowledge by capitalising on Large Language Models (LLMs) as well as human knowledge. You should have a PhD in
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, simulate complex societies, and help humanity learn from ten thousand years of recorded history. Historians and anthropologists work with AI collaborators that reconstruct lost evidence, test competing
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the Oxford–Novartis Collaboration for AI in Medicine. It is essential that you hold a PhD/DPhil (or are close to completion) in Statistics, Biostatistics, Statistical Machine Learning, or a closely related
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in theory of probability and statistics, machine learning, or formal methods. The post is available from 2 March 2026 until 1 March 2028. If you are still awaiting your PhD to be awarded you will be
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biological sciences and to solve major global challenges. We are seeking a Postdoctoral Researcher in Human-AI interaction to join a research group focused on studying learning and decision-making in humans
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of applying them to data. Collaborative endeavours with members of the IPMU and Oxford groups is highly encouraged. You will have the opportunity to teach. Applicants should have a PhD (or close to completion
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thrusts within the lab’s multi-agent security programme. You should possess a completed PhD/DPhil (or thesis submitted by the start date) in Computer Science, Machine Learning, AI, Security, Robotics
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the supervision and mentoring of junior researchers and doctoral students. It is essential that you hold a PhD/DPhil in Statistics, Biostatistics, Statistical Machine Learning, or a closely related quantitative
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outputs, and carry out collaborative projects with colleagues in partner institutions, and research groups. The successful candidate will have completed, or be close to completing, a PhD/DPhil in a relevant