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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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battery and optoelectronic laboratory Access to microscopy facilities Expertise of planning and executing measurements at beamline facilities worldwide Cooperation with a battery modeling software
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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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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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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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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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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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Environments (GLORIA ) and the Mountain Invasion Research Network (MIREN ). Project description The biodiversity crisis is in part driven by invasive species, yet many non-natives are only recognized as invasive
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of this development by exploring innovative approaches. You will be defining and extracting novel types of relations from event and tracking data. To analyze and interpret resulting spatio-temporal networks, you will
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. Our technological focus is thermal energy systems, sustainable carbon feedstock, and CO2 capture, utilization, and storage. Project background To achieve Switzerland’s net-zero climate target by 2050