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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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integrated with the NiMARE (NMA) software project. To be considered you will hold a relevant PhD/Dphil in statistics, machine learning or similar area, together with relevant experience working with brain
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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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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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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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students. Selection criteria: • PhD/DPhil in a relevant field (or near completion). • Strong project management and communication skills. • Multidisciplinary and collaborative mindset
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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
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or statistical machine learning. They will have excellent communication skills, including the ability to write for publication, present research proposals and results, and represent the research group at meetings
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We are seeking a full-time Postdoctoral Research Assistant in Machine Learning and Power Systems to join the Energy and Power Group at the Department of Engineering Science (Osney). The post is