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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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PhD in a relevant subject area. Excellent knowledge of machine learning and experience of applying machine learning techniques is expected. Familiarity with efficient ML techniques, edge AI hardware
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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
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with biophysical models and machine learning methods, to studies in both mouse models and patients. Applications are invited for a Research Associate to join the VISIONLAB, which is co-located in the Department
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-Dezfooli (Imperial). This is an exceptional opportunity to conduct ambitious research at the forefront of mathematics, statistics, (quantum) information theory, and machine learning. There are generous funds
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. with R and Python. Experience in machine learning. Background in molecular biology concepts and an interest in immunology. Passion for unravelling complex biological systems. Excellent communication and
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. Candidates are expected to have: An MSc or PhD degree in Computer Engineering, computational science-based discipline, or significant relevant experience. Experience writing and maintaining high-performance