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and implement multimodal retrieval with re-rankers for robust profile selection. Design and train advanced AI models for digital twin: 3D model learning, prediction models from imaging and molecular
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and machine learning based analyses including predictive modeling and real world evidence generation. Basic Qualifications: MS in computer science, biostatistics, biomedical informatics or related field
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model biases, and identify sources of predictability. The project will involve; 1) rigorous interrogation of NOAA GFDL's CM4X simulation output with respect to coastal sea level variability and relevant
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layered semiconductor characterized by an anisotropic crystalstructure and quasi-one-dimensional ribbon-like morphology. Its electronic structure is predicted to host relatively flat bands associated with
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responds to climate change in the past and present to improve future predictions of sea-level rise and Earth system feedbacks. The work combines collection of field data, remote sensing, and modelling in
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Portuguese version A call is open for the award of one Research Fellowship within the scope of the project “PROSPER: Predictive models for sustainable protein recovery”, funded by FEDER and by National Funds
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), multimodal vision and language models, and Large Language Models. Please find prior work here: (Google Scholar: https://scholar.google.com/citations?hl=en&user=oEifmSgAAAAJ&view_op=list_works&sortby=pubdate
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physical agent-based models, as well as the integrations of omic information to validate model predictions and developed in the context of the HPC environments at the BSC and at other HPC centres in Europe
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modeling to create a predictive tool that spans orders of magnitude in length and time. Hands-On Numerical Modeling: Implement your model in a custom-made data analysis tool that uses advanced optimization
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physics-integrated machine learning models—to predict, analyze, engineer, and understand microbial community dynamics. Applications span precision medicine and built environment microbiomes, with a strong