17 algorithm-development-"Newcastle-University"-"Newcastle-University" PhD positions at Maastricht University (UM)
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Description We are looking for a PhD-candidate interested in topics that lie on the border of optimization by the use of heuristic algorithms and (Explainable) Artificial Intelligence ((X)AI). Specifically, in
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: questionnaires on healthcare and patient outcomes, qualitative data on patient experiences, improved diagnostics using innovative laboratory methods, development of pathophysiology-based treatment plans
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, workshops, and collaborative writing projects. The tasks include: Developing a research plan, taking into consideration ethics and data management procedures of the Faculty; Literature review of published
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seek a motivated PhD candidate with an MSc and strong background in electrochemistry or engineering. The candidate will join a multidisciplinary project with KU Leuven to develop an electrochemical
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and research possible. PhD Opportunity: Engineering a Next Generation of Miniaturized In Vitro Models Our goal: Develop the next generation of miniaturized in vitro models to advance regenerative
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want to contribute to global health by helping develop ethical and equitable HIV cure strategies? Are you passionate about social science research with real-world impact? Then this PhD position might be
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will contribute to the development of experimental designs, run behavioral experiments and collect and analyze intracranial EEG data which provide a unique opportunity to get direct access to hippocampal
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developing personalized brain stimulation treatments. Your colleagues: You will be part of a collaborative, multidisciplinary team working at the intersection of brain stimulation , neuroimaging, and epilepsy
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. Artificial Intelligence (AI) is reshaping how organizations collaborate, make decisions, and develop talent. At the Graduate School of Business and Economics (GSBE), you will explore how AI transforms
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Intelligence (AI) and machine learning (ML) techniques. You will develop AI-based predictive models to anticipate user engagement, primarily using data collected through unobtrusive measurements (e.g., websites