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element of the values and vision of Leiden University. Leiden University is committed to becoming an inclusive community which enables all students and staff to feel valued and respected and to develop
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footprinting and/or socio-economic impact analysis of food consumption and production systems. Well-developed analytical and programming skills. Publications in respected scientific journals related
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inference. This framework comprises three main components: causal discovery, identification analysis, and experiment design. The causal discovery part learns a causal model of the environment from
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the Gaia Data Processing and Analysis Consortium (DPAC). The work will be embedded in Coordination Unit 5 (CU5, photometry) of DPAC, where close collaboration with the group at the Data Processing
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for innovative energy device integrations (proton/aqueous metal batteries, fuel cells, and reverse osmotic power generators), where the merits of ultrathin precision 2DHMs will result in the highest selectivity
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for innovative energy device integrations (proton/aqueous metal batteries, fuel cells, and reverse osmotic power generators), where the merits of ultrathin precision 2DHMs will result in the highest selectivity
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(with a strong mathematical focus) with above average grades Advanced knowledge in probability; expertise in random graphs, complex networks, hyperbolic geometry or topological data analysis is a plus
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social sciences or another relevant field; Qualitative and quantitative research skills (e.g., in data science, network analysis etc.) and a track record of innovative combination of research methods
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community is a central element of the values and vision of Leiden University. Leiden University is committed to becoming an inclusive community which enables all students and staff to feel valued and
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for an independent, motivated, and creative researcher with a strong background in deep learning architectures, and image analysis and interested in working on interdisciplinary project deeply rooted in biology, and