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Faculty of Engineering and Physical Sciences EPSRC Project Proposals 2026/27 (jobs.ac.uk) Project Link via the 'Apply' button above Project Title: Machine Learning Driven Corrosion Modelling in Bio
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announces an open competition for the position Ph.D. student - Machine learning-based tools for multiparametric enzyme optimisation Workplace: RECETOX, Faculty of Science, Masaryk University in Brno, Czech
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knowledge for a better world. You will find more information about working at NTNU and the application process here. About the position Distributed machine learning takes advantage of communication and
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28 Mar 2026 Job Information Organisation/Company Università di Pisa - Dipartimento di Informatica Research Field Computer science Researcher Profile Recognised Researcher (R2) Leading Researcher (R4) First Stage Researcher (R1) Established Researcher (R3) Application Deadline 11 Apr 2026 - 13:00...
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properties. Our group uses theoretical and computational chemistry, physics, and materials science in combination with chemical machine learning to explore and exploit diverse functional organic and hybrid
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Reporting to the PI, Prof Erin Saupe, the post holder will be a member of a research group with responsibility for carrying out research for ERC grant ‘Determining the drivers of extinction across space and time’. The post holder will provide guidance to less experienced members of the research...
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The Department of Electronic Systems at The Technical Faculty of IT and Design invites applications for PhD stipends or integrated stipends in the field of Machine Learning for Intelligent Hearing
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Position: Online Course Developer – RHIN 110 Clinical Data Science and Machine Learning Location: Remote (U.S.-based only) Division: Rabb School of Continuing Studies, Brandeis University
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24th April 2026 Languages English English English The Department of Electronic Systems has a vacancy for a PhD Candidate in Distributed Machine Learning Apply for this job See advertisement This is
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remain poorly understood. Their structural heterogeneity and chemical complexity make accurate atomistic modeling particularly challenging. Recent advances in machine learning approaches provide a powerful