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modelling. A background in plant ecophysiology and/or mass and heat transfer physics would be ideal but is not required. · You have numerical modelling experience. Experience with the programming
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Research or a related field Strong knowledge of quantitative and/or computational research methods, ideally in numerical optimization and simulation models. Proficiency in one of the major programming
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. Experience in working with animal models, cell isolations from tissues (lung, intestine), human tissue samples, multi-parameter flow cytometry, molecular biology and fluorescence imaging will be preferred. We
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foundation-model experiments. Your role We are seeking a highly motivated Postdoctoral Researcher to join the FNR AI-HPC 2025 BRIDGES project GenePPS, which investigates how machine learning can enable
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diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular
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of conceptual and numerical models describing sediment material properties, sediment and water transport, leveraging in-situ and indirect measurements such as geophysical and/or remote sensing
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academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular Networks, and
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to work with the FLEXPART model, you can also propose your own research topics. These could be, for instance, theoretical and numerical improvements of FLEXPART, development of new application fields
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academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular Networks, and
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of the Di Angelantonio-Ieva group in the HDS Centre. Your mission As Postdoc you will focus on developing and applying state-of-the-art generative models (such as VAEs, GANs, and transformer-based