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the use of hierarchical graph neural networks for modeling multi-scale urban energy systems. By combining advances in Physics-Informed Machine Learning (PIML) and Graph Neural Networks (GNNs) with real
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Description As part of a new EU MSCA Doctoral Network ELEVATE (101227453), we are offering a PhD Position on “Modulating bursting activity in silicon neurons to control plasticity and attention”. Location
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and the underlying genetic networks through comparative transcriptomics analyses. The work will involve fieldwork in East Africa and laboratory research in Switzerland, with additional visits
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, networking opportunities, and competitive salaries. The position is available from January 2026 or after negotiation with a duration of four years. We live a culture of inclusion and respect. We welcome all
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Group in the Social Networks Lab at ETH Zürich is developing network data science methods and applying them in a variety of domains. Our current research focus is on sports analytics and we seek to expand
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, and this process is tightly regulated by a network of chaperone proteins. At the heart of this network is Hsp90, a molecular chaperone essential for the folding and maturation of at least 20% of all
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across EPFL’s international research network. Profile Master’s degree in environmental engineering, microbiology, chemical engineering, or a related discipline Demonstrated research experience
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expertise will extend to various areas, including quantum Monte Carlo, machine learning, quantum computing, quantum machine learning, and tensor networks. These and other techniques will allow us to confront
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on musculoskeletal biomechanics and mechanobiology across all scales. Project background This project is part of the EU-funded Print4Life , a Marie Sklodowska-Curie (MSCA) doctoral network led by Prof
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Marie Curie Grant Agreement Number 101226997 Is the Job related to staff position within a Research Infrastructure? No Offer Description Background Information Marie Skłodowska-Curie Doctoral Networks