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learning, at the intersection of reinforcement learning, deep learning and computer vision, in order to train effective robotic agents in simulation. You should hold a relevant PhD/DPhil (or near completion
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projects in computer vision research, with a particular emphasis on Spatial Intelligence, 3D Computer Vision, and 3D Generative AI. You should hold a relevant PhD/DPhil (or near completion*) in Computer
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PhD in Chemistry or a relevant subject area, (or be close to completion) prior to taking up the appointment. The research requires experience in computational chemistry, including machine learning
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evaluations, attacks 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
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a PhD/DPhil or equivalent in a quantitative discipline such as computer science, statistics, machine learning, statistical or population genetics, or a related field. They should have experience in
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We are seeking a full-time Postdoctoral Research Associate in Machine Learning for Grid-Edge Flexibility to join the Power Systems Architecture Lab within the Department of Engineering Science
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projects. It is essential that you hold a PhD/DPhil in a quantitative or computer science related subject (e.g. Statistics, Machine Learning, Biostatistics, AI, Engineering), and have post-qualification
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learning (IRL) techniques. You should have a relevant PhD/DPhil (or be near completion), together with research experience in inverse reinforcement learning (or related machine learning techniques
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be considered. You should possess a PhD or DPhil (or near completion of) in Computer Vision or Machine Learning. You should have knowledge of approaches for areas related to efficient, reliable, and
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PhD/DPhil in Computer Vision or Machine Learning (or be near completion) and hold a strong publication record in this field. You must have sufficient theoretical and practical knowledge of methodologies