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At the Faculty of Medicine, Department of Health Science and Technology, one or more PhD stipend’s are available within the doctoral programme: Biomedical Engineering and Neuroscience from June 1
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Your position Our group conducts research at the intersection of artificial intelligence (AI) and pediatric healthcare, developing AI and machine learning (ML) methods to address real-world clinical challenges. Our core research topics include but not limited to the following...
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Apply now The Faculty of Science and the Leiden Institute of Advanced Computer Science (LIACS) are looking for: PhD Candidate, Reinforcement Learning for Sustainable Energy This position is embedded
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Carl von Ossietzky University of Oldenburg • | Oldenburg Oldenburg, Niedersachsen | Germany | about 8 hours ago
strategies for their present and future careers in science and enterprises. The core of the programme is an independent doctoral research project in one of the participating research groups. In
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, computational modelling, bioinformatic analysis, and experimental vascular biology. Based in a dynamic translational research environment of data-driven life science, computational imaging, and vascular surgery
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Emulators of Stochastic computational models"), funded by the Swiss National Science Foundation (SNSF). The project aims to significantly advance the state-of-the-art in uncertainty quantification (UQ) by
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At the Technical Faculty of IT and Design, Department of Sustainability and Planning, a PhD stipend is available within the general study programme in Development and Planning. The stipend is open
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degree in statistics, computer science, data science or computational biology. Ideally, the applicant would have a background in both biology/biomedical sciences and statistics/data science. Applicants
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program Data-driven life science (DDLS) uses data, computational methods and artificial intelligence to study biological systems and processes at all levels, from molecular structures and cellular processes
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Emulators of Stochastic computational models"), funded by the Swiss National Science Foundation (SNSF). The project aims to significantly advance the state-of-the-art in uncertainty quantification (UQ) by