113 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "Mines Paris PSL" research jobs at University of Oxford
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, machine learning, and/or computational biology to be able to work within established research programmes. They will have excellent communication skills, including the ability to write for publication
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and machine learning systems led by Prof Christopher Summerfield. The post-holder will have responsibility for carrying out rigorous and impactful research into human-AI interaction and alignment, with
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Machine Learning (OxCSML) research group, with responsibility for leading and carrying out research pertinent to the project, as well as day-to-day management of research activities relevant to the project
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We are looking for a Postdoctoral Research Associate reporting to the Principal Investigator Prof Yee-Whye Teh, they will be a member of the Oxford Computational Statistics and Machine Learning
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willingness to learn new skills and undertake further training and development aligned to the role, which may include working with laboratory animals Experience in working in a scientific laboratory, including
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publishing work as lead author. Experience with machine learning methods for modelling human learning, such as knowledge tracing and/or experience with conducting research that involves prompting or fine
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) in Physics or a related field. Previous experience in cosmological simulations, analysis of cosmic microwave background and/or large-scale structure datasets, machine learning methods applied
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responsible for supporting the delivery of various foresight research projects the Centre will be undertaking. This is an excellent opportunity to gain academic research experience and to learn from leading
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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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sciences, AI, machine learning or related fields. Strong background and track record in the development of geospatial foundation models from multi-modal Earth Observations is essential as well as strong