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for realizing challenging asymmetric catalytic methods. This network brings together academic research groups with expertise in experimental catalyst development and theoretical groups skilled in computational
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project focuses on developing methods to assess the sustainability of nearly 100 nationwide AI systems within the National Lab on Education and AI (NOLAI). You will create methods to predict energy
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newly collected survey data, which you will link to CBS register data, using advanced statistical methods. The data collection will be executed by KBA in collaboration with NCO, but you will be involved
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assisting in courses. Requirements Desired profile A master degree in computer science, artificial intelligence, or (very) similar. Self-drive, creativity, rigor, sense of ownership, and excitement to push
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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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trends. Data-driven approaches are attractive alternatives. Descriptors are used to characterize the molecular properties of catalysts together with statistical methods to derive predictive models
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methods to administrative data. The exact topic will be chosen in coordination between the successful candidate and supervisors. The successful candidate will be part of the Strategy Economics group within
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the electrochemical active site and impact on transmembrane proton translocation. The project aims to develop new methods and devices routed on the chemistry of 2D materials and the functionalization of graphene
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to participate in field experiments. The PhD position is embedded in the research programme Marketing of FEB’s Research Institute. You will therefore be working at the Department of Marketing. The project will be
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Apply now The Faculty of Science and the Leiden Institute of Advanced Computer Science (LIACS) are looking for: PhD Candidate, Reinforcement Learning for Sustainable Energy This position is embedded