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groups working on digital health and wellbeing , network science , computational social science , and various topics in machine learning. You will be working in the research group of one of the PIs
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The University of Oxford is seeking a highly motivated Postdoctoral Scientist with expertise in biostatistics, machine learning, and cardiac magnetic resonance imaging (MRI) to join Professor Betty
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following: Quantum chemistry (preferably of excited states) Multiscale simulations/environmental modelling Excited state dynamics Data Science/Machine learning in chemistry/Cheminformatics Molecular dynamics
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, and you will join the research group led by Prof. Christa Cuchiero to work at the intersection of Mathematical Finance, Stochastic Analysis and Machine Learning. The research areas cover a wide range of
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backgrounds, including computational chemistry, bioinformatics, systems biology, and machine learning. The project offers a unique opportunity to collaborate closely with experimental scientists and contribute
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researchers in applied mathematics and machine learning. This is due to its remarkable flexibility, mathematical elegance, and as it has produced state-of-the-art results in many applications. As a leading
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intelligence; can present at national and international conferences; and prepare manuscripts as first or joint author for submission to leading journals and conferences in machine learning, cancer research and
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should have experience of working with computational and analytical techniques in the areas of natural language processing (including, among others, topic modelling), computational linguistics, and machine
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state-of-the-art machine learning and deep learning techniques (such as generative adversarial networks), with empirical fieldwork in Norwegian glacier environments. As a Postdoctoral researcher, you will
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the development of hierarchical computational materials discovery schemes combining random structure searching, machine learning, atomistic, and density functional theory (DFT) calculations to accurately and