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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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, 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 with a
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geopolitical world. A key part of the training element of this network is the use of techniques and tools that cross the three dimensions that articulate the network –politics, policy and partners. This includes
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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