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regarding the demand that will have to be met soon. This requires building robust models to predict future demand, making decisions under time heterogeneous uncertainty and adapting to the state
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PhD in Data-Driven Modeling of Homogeneous Catalysts (1.0 FTE) (V25.0035) « Back to the overview Job description Among the most challenging to develop catalytic reactions are stereoselective
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collected in these trials will feed into the development of the Human Dietary Digital Twin—a computational model for the prediction of individual metabolic responses to complex meals. This modeling work will
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-year PhD program, you will join a collaborative research team applying cutting-edge methods from Experimental/Behavioral Economics alongside modern macroeconomic modelling techniques (e.g. DSGE). You'll
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energy system modelling is critical for reducing emissions, enhancing resilience, and integrating high shares of renewable energy sources. Such modelling provides essential tools for designing low-carbon
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properties of catalysts together with statistical methods to derive predictive models for selective catalysis. In a data-driven approach, an initial set of reactions is analyzed and used to establish such a
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complex urban settings. Together with researchers from the fields of energy modelling, social network analysis and governance, you will develop new spatial energy concepts to integrate the social
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’, i.e. policies that deal with problems after they occur, rather than long-term prevention. By developing innovative simulation models that incorporate the life-course consequences of policy options, your
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early steps in lung tumor development. You will use cutting-edge techniques including lung organoid models, single-cell transcriptomics and epigenomics, and CRISPR-based genetic perturbation. Key
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theoretical problems. Good computational/programming skills in Matlab or Python High proficiency in written and spoken English Research at our Faculty The Faculty of Science is a world-class faculty where staff