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of the research interests of your prospective supervisor from her homepage. You will be joining an active and growing group around noncommutative geometry and functional analysis, within a larger group in Leiden
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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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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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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
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, which will involve a combination of nanofabrication, nanoscience, chemistry, instrumentation development and tunneling signal analysis. You will work in a highly collaborative environment as part of a
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the European Research Council and led by Dr Jasmijn Rana. About the project Through a comparative analysis of participation in outdoor recreation in Europe, DivOut examines how social inequalities are embodied