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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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(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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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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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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. 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
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not essential, as training is available from the Cambridge Centre for Teaching and Learning (CCTL) . Note that this opportunity is only open to University of Cambridge PhD students (and PostDocs). You