2,920 machine-learning "https:" "https:" "https:" "https:" "The Open University" PhD positions
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Stein bei N rnberg, Bayern | Germany | 29 days ago
programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Area of research: PHD Thesis Job description:PhD Position in Machine Learning for Single-Cell Genomics (f
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application! Your work assignments We are looking for a PhD student to work on the development of novel spatio-temporal machine learning methods. Our world is inherently spatio-temporal, i.e. physical processes
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next-generation machine learning (ML) models that are both data-efficient and transferable, enabling more reliable catastrophic risk prediction, defined as the probability of exceeding critical safety
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, including machine learning and language technologies, for the integration and analysis of clinical, advanced data harmonisation, and next generation research infrastructures. You will contribute to research
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Mathematics (Inverse Problems), Computer Science (Machine learning, Efficient Algorithms and High-Performance Computing), and Physics (Image Formation Modelling). Your project is part of the NXTGen High-tech
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remains challenging, limiting multiscale imaging approaches in near-field holotomography. To address this, the PhD project combines machine learning, high-performance computing, and synchrotron-radiation
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25th February 2026 Languages English English English The Department of Materials Science and Engineering has a vacancy for a PhD Candidate in machine learning and large language models (LLMs
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Description The overarching mission is to conduct research combining machine learning, data assimilation, and physical modeling to enhance short-term (days/weeks) forecasts of Arctic sea ice conditions. The
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About the project: From Brittle to Ductile: Machine Learning 3D Fracture Simulations for Extreme Environments Supervisor: Prof, James Kermode, University of Warwick Develop cutting-edge machine
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samples, phase retrieval in this regime remains challenging, limiting multiscale imaging approaches in near-field holotomography. To address this, the PhD project combines machine learning, high-performance