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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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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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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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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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independently and in a collaborative, interdisciplinary environment. Excellent communication skills in English (both written and spoken). We are seeking a motivated and enthusiastic PhD candidate who is eager
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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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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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analytical and problem-solving skills with high scientific curiosity. Ability to work independently and in a collaborative, interdisciplinary environment. Excellent communication skills in English (both
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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