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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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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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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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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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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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and mateRials, fabrication techNologies for the responsible co-creation of future Sustainable integrated electronic systems), an EU MCSA Doctoral Network with 15 funded 3-year PhD positions in parallel
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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
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to reduce greenhouse gas emissions and achieve net-zero targets. PhD position in Data-driven monitoring of CO2 and NOx emissions from space Your tasks Simulate emission plumes from point sources using
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field interactions Tuning of the CFD models with experimental results Artificial Neural Network training and development Scientific publications in journals and at conferences Supervision of students Your
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to ensure project success. Additionally, Empa offers outstanding infrastructure, career support, networking opportunities, and competitive salaries. The position is available from January 2026 or after