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We invite applications for a fully funded PhD position in the field of numerical modelling of iron electrodeposition, i.e., multiphase flows involving phase change, using fully resolved CFD methods
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Prof. Olga Fink (EPFL IMOS) and the UESL team at Empa, combining cutting-edge expertise in machine learning and energy system modeling with strong ties to academic and industry partners. The PhD is
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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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sciences, economics and regulation. Job description The project of the PhD student based at CWI in Amsterdam will focus on techno-economic models (and in particular multi-agent modeling) of energy exchange
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renewable energy generation.KU Leuven leads Modelling and Optimization, which focuses on: Developing hybrid models combining first-principle and machine learning approaches. Creating predictive frameworks
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to distributed energy resources, enabling energy sharing, reducing grid congestion, and enhancing sustainability. The PhD project will focus on the governance models adopted for energy hub platforms. These include
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This interdisciplinary project uses advanced stem cell-based embryo models, called blastoids (developed in the laboratory of Prof. Vincent Pasque), which mimic the early human blastocyst stage. Your work will focus
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cell (and one cell–cell interaction) at a time. You will work with large-scale single-cell and spatial transcriptomics data to develop and apply single-cell foundation models — generative machine
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machine learning approaches. These are similar to earlier work on charge and excitation energy transfer (see https://constructor.university/comp_phys). The project for the PhD fellowship is slightly more
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-specific TEM protocol will be used to reveal their subcellular localisation. This work will be done at UEA and IOCAS (Prof. Shan Gao). Objective 2: Using the genetically tractable model diatom Phaeodactylum