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microbial metabolites and its effect on chronic kidney disease and cardiovascular complications, using an in vivo model of chronic kidney disease. Responsibilities and qualifications As a PhD student, you
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mathematical foundation of machine learning models. You will be responsible for developing scientific machine learning methodologies enabling new approaches for solving machine learning problems including
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, including electrical engineering, control theory, industrial engineering, electronics engineering, energy policy, data science, and applied mathematics. As part of the Alliance program, your project will be
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functional priors from billions of years of evolution; how to compress measurements with controlled mixtures of molecules; and how to align models of laboratory experiments with observational human biology
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designs, building effective and conceptual models to inform our theoretical understanding, and developing code and theory frameworks to address new topological phenomena. Depending on the project’s results
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, social networks, fairness, and data ethics. Our research is rooted in basic research and centres on mathematical models of the physical and virtual world, as a basis for the analysis, design, and
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collaboration within e.g. nutrition, chemistry, toxicology, microbiology, epidemiology, modelling, and technology. This is achieved through a strong academic environment of international top class with
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Applied Thermodynamics, Transport Processes and Mathematical Modelling. The center has recently entered dynamically into Biorefinery Conversions research field. The focus of Biorefinery Conversions research
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scientific backgrounds, including electrical engineering, industrial engineering, operations research, data science, and applied mathematics. Many of our former students are now successful scientists in both