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Field
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-Physical Energy Systems The PhD position focuses on the development of secure and trustworthy AI for resource-constrained embedded systems. The research will investigate how machine learning models can be
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, through course information or thesis deliverables in the application Skilled in either computational modelling or experimentation, paired with the willingness to work on both Personal characteristics
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combination of biochemical and molecular approaches in cultured cells, xenograft models, and genetically engineered mice. The ideal candidate should have valid authorization to work in the United States and a
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Are you an enthusiastic early career researcher who is keen to work on integrated environmental assessment, offshore wind energy systems, biodiversity impacts, and life cycle assessment (LCA
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power systems, energy conversion, modelling, simulation and programming tools (e.g. MATLAB, Simulink, Python), or related research areas will be considered an advantage. Strong analytical skills, a
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focus We welcome candidates with a strong interest in developmental biology, organoid technology, and disease modelling. This position is part of the EU-funded Marie Curie doctoral network (VISI-ON-BRAIN
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, and how strain rate influences material behaviour. The resulting experimental data will be used to validate numerical models and contribute to certification by analysis of future composite structures
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of materials modelling and nuclear theory, and the ability to work both independently and collaboratively in an international research environment. Where to apply Website https://phd.fbk.eu/calls/detail/ab
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the scope of R&D projects CFD4SynGAS - Optimizing syngas yield via CFD modeling of co-gasification of biomass and Refuse-Derived Fuels for Waste-to-Energy solutions, 2024.14779.PEX. The complete notice is in
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, 115114. - M. Billoir, D. Crepin, S. Plaszczynski, B. Grammaticos, O. Seksek & M. Badoual (2025) The temporal response of a glioma cell population to irradiation: modelling the effect of dose and cell