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), whose objective is to extend the HLA-Epicheck model, originally developed within the framework of a PhD thesis, and to implement new deep learning approaches to assess donor–recipient compatibility in
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and apply state-of-the-art modelling, characterisation, and machine learning techniques to understand how batteries behave and age. Collaborating with project partners, you’ll turn these insights
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novel machine learning models—including Physics-Informed Neural Networks (PINNs), variational autoencoders, and geometric deep learning—to fuse multimodal data from diverse experimental probes like Bragg
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and have a PhD in a field related to mathematical modeling and experience in optimizing industrial processes, this might be something for you! We are looking for a postdoctoral researcher (“PostDoc”) to
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will be compared to natural prototypes (e.g. in Asia or western North America) and analog models, with emphasis on the implications for structural models needed for energy geoscience. Key skills include
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validation. You will be joining a strong team that works within the following domains: Dynamic turbine response modelling in the multi-body framework HAWC2 Aero-servo-hydro-elastic stability analysis
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machine learning models that predict soil health and crop performance. The position will exploit datasets integrating biochemical and molecular soil parameters (with a focus on microbiome features from
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to detector/system modeling and optimization for count rate, resolution, and throughput. Document methods and develop user-facing procedures and best practices for reliable operation during user runs
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Position Details Position Information Internal Posting? Posting Number SP005013P Position Title Postdoctoral Research Associate, Predictive Modeling for Mental Health Outcomes Division/College
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make a difference in the world! Position Information The selected candidate would assist a team of ecosystem modeling scientists to perform process-based ecosystem models based research and develop