159 machine-learning-"https:"-"https:"-"https:"-"CEA-Saclay" Postdoctoral positions in United Kingdom
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Samuel Kaski’s research group Probabilistic Machine Learning is searching for postdocs to work on AI fundamentals in exciting projects. The work includes collaboration with ELLIS Institute Finland
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Vision or Machine Learning. You should have a strong publication record at the principal international computer vision and machine learning conferences and should hold sufficient theoretical and practical
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the next generation of PV technologies for beyond 2030. The new postdoctoral research position will use materials modelling techniques (DFT, molecular dynamics, machine learning potentials) to investigate
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About the role We are seeking a full-time Postdoctoral Researcher to join the Oxford Secure and Advanced Computer Architecture Research (OSCAR) group at the Department of Engineering Science
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of Engineering Science. The post is funded by EPSRC and is fixed term to the 31st January 2027. A2I explores core challenges in AI and machine learning to enable robots to robustly and effectively operate in complex, real
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frameworks for quantum machine learning, including conformal quantum prediction and uncertainty quantification in quantum models; Theoretical and algorithmic advances rooted in statistical learning theory
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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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well as companies and governmental organisations . They will contribute to the activities of the wider machine learning and data science research group and write up the results of their work, with co-authors
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and decision-making in humans and machine learning systems. The post-holder will have responsibility for carrying out rigorous and impactful research into human-AI interaction and alignment, with a
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the scientific investigation of artworks and historical objects. The project aims to advance the mathematical foundations of imaging and machine learning while directly supporting research in art history