63 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "UCL" Postdoctoral positions at University of Oxford in United Kingdom
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The postdoctoral researcher will lead the development of computational methods for aligning cortical organisation across species using transcriptomic and anatomical data combined with modern machine
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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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) 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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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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students in the group. Candidates should have strong training in cross-disciplinary applied mathematics, with a demonstrated interest in biology, and experience in machine learning approaches is a plus. We
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An exciting opportunity has arisen for a Postdoctoral Research Assistant in the Department of Physics. Machine learning has made enormous progress during recent years, entering almost all spheres
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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