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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
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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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multiagent dynamics, with special focus on human decisions and opinion dynamics. The research will deal with both theoretical and computational aspects. The student will develop dynamical models and apply them
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multiplex analysis. We will assist the computer scientists to apply artificial intelligence Machine Deep Learning models using the omics data of mitophagy to predict risk of cancer and metastasis and design
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and verification of computation technology for model-based analysis and optimization of process systems, in this case systems for managing stormwater in extreme situations. The work is carried out in
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landscape, the focus of this position is on process design, system integration, and optimization of performance. Teaching at first, second and third cycle level is a central part of the department's mission
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and development centre – an excellent infrastructure to develop and optimize membrane processes from lab to pilot scale. The project is part of COMPEL. COMPEL, "COMPetence for the ELectrification
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description The subject of Energy and Environmental Engineering is built up and developed by the research in the research specialisation of Future Energy. This research programme is mainly technologically
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SLAMF6, to understand the mechanisms behind their influence on T-cell activation. This research is relevant to optimizing the utilization of these receptors in cancer therapy. The project is highly
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application! Work assignments Subject area: Computational studies of the influence of microstructural features on the structural integrity of metallic materials using machine learning Subject area description