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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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more cost-efficient. Together, UESL and IMOS are seeking a motivated and qualified PhD candidate to advance the use of hierarchical graph neural networks for modeling multi-scale urban energy systems. By
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development of electrochemical sensors detecting environmental pollutants, providing real-time information for effective management. Past and current work includes electrochemical sensors for airborne virus
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. The group Multiomics for Healthcare materials at Empa, St. Gallen generates and integrates multi-modal biomedical datasets with the aim to inform development of new sensors, support nanoparticle-based
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and flow field interactions Tuning of the CFD models with experimental results Artificial Neural Network training and development Scientific publications in journals and at conferences Supervision
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multifractal analysis, urban and energy planning, geography, and artificial intelligence to develop coherent and resilient approaches for urban energy infrastructures under land-use constraints such as No Net
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crucial insights. In this project, you will contribute to the development of AI-driven methodologies for experimental fluid mechanics , focusing on: Designing multi-fidelity neural networks for adaptive
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, geography, and artificial intelligence to develop coherent and resilient approaches for urban energy infrastructures under land-use constraints such as No Net Land Take. The consortium comprises four
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Network with 15 funded 3-year PhD positions in parallel. Your profile Master Degree in environmental/natural sciences or engineering, or similar. Experience with developing computational models Preferably
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missions (e.g., CO2M, TANGO, Sentinel-4/5). Your research will contribute directly to monitoring global efforts to reduce greenhouse gas emissions and achieve net-zero targets. Your tasks Simulate emission