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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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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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-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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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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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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to characterize the molecular 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
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. Data-driven approaches are attractive alternatives. Descriptors are used to characterize the molecular properties of catalysts together with statistical methods to derive predictive models for selective
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, analysing the data, developing prognostic and simulation models and writing scientific articles. The PhD position is embedded in the research programme Operations of FEB’s Research Institute, while building
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Fellowship Programme (EVOLVE – EVOLVE Fellowship Programme), an initiative by six world-leading research institutes of two universities in The Netherlands (University of Groningen, Leiden University) to study
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control research. This project aims to address this gap by examining (1) the types of management controls used by MFIs, (2) how these controls interact across different MFI models, and (3) their role in