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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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14 Mar 2026 Job Information Organisation/Company Empa Research Field Chemistry » Analytical chemistry Chemistry » Biochemistry Chemistry » Computational chemistry Chemistry » Inorganic chemistry
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15 Jan 2026 Job Information Organisation/Company Empa Research Field Computer science » Other Engineering » Other Mathematics » Applied mathematics Mathematics » Statistics Researcher Profile First
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of Computational Fluid Dynamics CFD environment and simulations including: - Computation of the microwave field, Coupling of the microwave field with the plasma - Computation of elementary ionization, recombination
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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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with project partners and disseminate results through reports, visualisations, and scientific publications Your profile You hold an M.Sc. in Data Science, Computer Science, Engineering, Physics
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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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14 Feb 2026 Job Information Organisation/Company Empa Research Field Computer science » Other Engineering » Other Mathematics » Applied mathematics Technology » Energy technology Technology
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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