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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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electrode with free flow channels. The successful candidate will work on cutting-edge research combining computational modelling with experimental validation. The project involves developing meso- and
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(Se) cycles through the atmosphere, from emissions, chemical transformations and transport to deposition. The project will integrate field sampling, lab analyses, and computational atmospheric modeling
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computational modelling with experimental validation. The project involves developing time- and position-dependent models of electrochemical double layers, and parameter estimation methods incorporating
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). Hands-on laboratory experience with lasers, optics, or photonic devices. Skills in experimental data acquisition and analysis (in Python). Motivation to combine computational modeling with experimental
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through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description PhD position: Mapping CO2 Capture
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(finite element modeling, computational fluid dynamics), and proven experience with COMSOL Multiphysics. Knowledge of heat and mass transport processes in heat-sensitive materials and process optimization
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continuum modeling (finite element modeling, computational fluid dynamics), and proven experience with COMSOL Multiphysics. Knowledge of heat and mass transport processes in heat-sensitive materials and
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scanning probe instrumentation fabrication of van der Waals heterostructures transport, optical spectroscopy, and quantum sensing experiments data analysis, modeling, and scientific communication
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thermoelectrics. QUALIFICATIONS AND ELIGIBILITY The ideal candidate has a university master’s degree in computational science, physics, or chemistry and is passionate about analytical and numerical modeling and