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platform for metal AM parts; 2)develop and perform advanced data-processing techniques (e.g., data-driven modeling with embedded nonlinear dynamics) for vibrational feature extraction; 3)conduct quality
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cycles to continuously improve models via active learning and guide evolutionary trajectories toward promising but otherwise inaccessible sequence spaces. You will be embedded in one of the three research
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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
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computational models that learn by finding patterns in language data. Can they learn the different modal meanings simply by paying attention to what they “hear”? Or do they need to come prepared with certain
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previous studies have yielded a wide array of conceptualizations and insights, research on this complex issue still suffers from a lack of sufficiently detailed conceptual models, from a paucity of insight
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mechanism to cope with a rapidly changing world”, with the Seychelles warbler (Acrocephalus sechellensis) as a model system. The project is coordinated by Prof. Jan Komdeur (see: https://research.rug.nl/en
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, business school scientists, system modeling and optimization researchers, computer scientists, legal experts and social scientists working on energy topics. Description of the PhD project The project
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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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. 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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approaches, you will establish accelerated Design-Build-Test-Learn cycles to continuously improve models via active learning and guide evolutionary trajectories toward promising but otherwise inaccessible