979 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "Simons Foundation" research jobs in United Kingdom
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leave schemes Cycle and electric car loan schemes Employee Assistance Programme Membership to a variety of social and sports clubs Discounted bus travel and Season Ticket travel loans How to apply: Please
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collaborate with other technical groups working on the design. The successful candidate will also have opportunity to conduct experiments and machine development activities on the existing accelerators. The key
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/diffusion tensor MRI. Experience with machine learning/deep learning/AI methods. What we can offer you: Be part of a team developing cutting-edge simulation technology having real-world impact. Grow your
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deep learning models, in causal statistical models and in human-machine teaming and AI ethics. The researcher will conduct internationally-leading research in human factors with applications
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. Experience solving diffusion or related equations by Monte Carlo methods. Knowledge of MRI/diffusion tensor MRI. Experience with machine learning/deep learning/AI methods. Be part of a team developing cutting
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, advanced sensing techniques, sensor and operational data fusion, data analytics, and machine learning algorithms for condition monitoring, fault diagnosis, and early fault prediction in electric vessels
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deep learning models, in causal statistical models and in human-machine teaming and AI ethics. The researcher will conduct internationally-leading research in human factors with applications
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laboratory scientists, computational biologists, and clinical academic colleagues, running computer simulations and testing predictions in the lab. About you You should be highly motivated, and ambitious
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machine learning with rigorous statistical methods to address fundamental questions in cosmology and beyond. The research aims are to develop novel inference frameworks for cosmology and apply them
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language processing for accessible science to contribute to the Terminology-Aware Machine Translation for Accessible Science (TaMTAS) project. The project is funded by EPSRC under the CHIST-ERA Call 2025: Science in