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regimes. This PhD project aims to develop predictive pore network models integrated with thermodynamics and upscaling methods toward reservoir-scale applications. We seek candidates with a strong background
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based on Machine Learning (ML) emulators have taken the weather predictions research by storm, as they run faster and use less energy than traditional approaches: numerical models based on physical
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to identify those most at risk from extreme heat, as well as offering personalized adaptation advice --- translating rich multi-modal data into interpretable, scalable prediction and advising models. ICARUS
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the mechanisms of sorption and diffusion; (iv) to establish relationships between molecular structure and adsorption properties; and finally (v) to combine experiments and simulations to predict the performance
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, resilience and evolution of marine life to develop solid theories and predictive models of the relationships between marine biodiversity and ecosystem functions, which will in turn lead to improved economic
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teams. Unit URL https://imci.uidaho.edu/ Position Qualifications Required Experience Experience with statistical or predictive modeling as demonstrated by publications in the field Evidence of strong
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PhD MSCA - Acoustic and Ultrasound-based Predictive Maintenance Systems for Industrial Equipment Power converters are essential in numerous applications such as industry, photovoltaic systems
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Post-doctoral position (M/F) for testing drought-based BEF relationships at CEFE Montpellier, France
) Carry-out additional simulations with the Phoreau model to test the effect of tree diversity on forests' resistance to droughts. ii) Analyse biodiversity-drought resistance relationships, across a
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to data analysis, feature engineering, model development, evaluation, and documentation, while progressively gaining exposure to production systems, client-facing work, and modern AI practices across
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:this project pioneers a new paradigm of General Genome Interpretation (GenGI) models by combining DNA Large Language Models (DLLMs) with Deep Neural Networks to predict human phenotypes directly from Whole Exome