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telemetry equipment. They will be responsible for ensuring high quality twins, and will interact with the project team on the development of effective machine-learning models to be deployed within these twin
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grant funding remaining in place. Holding a PhD in Music or a related discipline and with a record of, or clear potential for, outstanding research, you will be a scholar whose research activities fall
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We are seeking a highly creative and motivated Postdoctoral Research Assistant/Associate to join the Machine Learning Group in the Department of Engineering, University of Cambridge, UK. This
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. The PDRA should have a relevant specialist area in data science, including managing and structuring data, programming, developing and applying theoretical methods and machine learning models for data
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coincident with ever more demand for earth and sky monitoring. This PhD project aims to develop and apply the cutting edge of Bayesian analysis and machine learning to the optimisation of satellite
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the use of machine learning to tackle major scientific challenges. Working across disciplines within the University, Accelerate is advancing research at the interface of AI and science, providing training
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Research Assistant / Research Associate in Frontiers of Atomistic Simulation Techniques (Fixed Term)
interested in the application and methodological development of machine learning techniques to combine electronic structure and modern sampling techniques in liquid-phase heterogeneous catalysis. Experience in
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of interest in the group, which include: Topological materials Superconductivity Strongly correlated materials Exciton-phonon coupling Machine learning Further details about our areas of interest can be found
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data-driven big data and machine learning approaches. The Research Associate would be responsible for undertaking the study in Cambridge, as well as some aspects of study co-ordination or data analysis
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(healthcare, clinical trials). The research will involve working on research cohort and clinical data, applying machine learning models to synthesise biological (brain imaging, genetic) and cognitive