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fundamental research, we create widely used open-source software including autodE, cgbind/C3, and mlp-train. Our recent advances in Machine Learning Interatomic Potentials (MLIPs) form the foundation of our ERC
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situ advanced microscopy of fibre‑based materials. The project aims to develop and deploy machine learning tools that extract real‑time structural and chemical information, enabling deeper understanding
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imaging datasets and advanced machine learning approaches to identify novel imaging markers of mental health disorders and cognitive function; 2) developing robust MRI-based acquisition, image
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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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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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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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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 sequence of the human genome and the development of common diseases. You will work on a collaborative project that aims to develop Machine Learning and laboratory-based approaches, for decoding how the human
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turbines represented using an actuator-line approach, assess the applicability and limitations of reduced-order models in predicting turbine performance, and develop machine-learning surrogate models capable