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modelling to analyse research data with variability at different levels or clustering Knowledge of missing data analysis using multiple imputation Experience and/or knowledge of health economics analyses
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the University work every day. Its portfolio includes organizations with diverse financial operating models—from institutional and service-supported operations to revenue-generating and cost-recovery units—and
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, Machine Learning, or related areas. - Knowledge of Large Language Models and Retrieval-Augmented Generation. - Experience or interest in Knowledge Graphs, Semantic Web technologies, information retrieval
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their website at https://www.naritalab.com/ Our laboratory investigates the biology of preneoplastic cell states, with particular interest in how changes in cell functional identity and plasticity are controlled
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materials and structures. By combining numerical modeling, laboratory experiments, and theoretical analyses, we seek to link microscopic processes with the macroscopic behavior of both engineering and natural
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recommendations for leadership. Experience implementing new HR policies, systems, programs, service models, or workforce initiatives in partnership with central HR or institutional leadership. Advanced knowledge
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modelling (numerical simulations, e.g. matlab or python, or analytical modeling), OR optomechanics, OR high frequency devices. You are eager to learn and expand your knowledge! Basic knowledge of analog and
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Group Leader and Professor AI in Biology - Dept of Computer Science and Dept. Electrical Engineering
interested in recruiting faculty members who use and develop artificial intelligence methods and mechanistic mathematical models to address fundamental questions in biology. Examples of research topics include
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mechanisms and developing disease models to preclinical therapy testing, clinical readiness and patient-informed treatment priorities. To achieve this, POLARIS brings together expertise in leukodystrophy
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by combining machine learning with in-depth knowledge of biological processes. We aim to work towards foundation models and integrative theories of biological systems, and towards innovative AI-driven