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: comparative omics, genetic diversity analysis, mathematical modelling, machine learning, and the use of model organisms. Develop transferable skills such as scientific communication, project management
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 10 days ago
and fine-grained semantic information within the prompts, and assess geometric accuracy of corresponding models' answers. If necessary, we will then propose dedicated learning strategies for inducing
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; Independent/collaborative development and deployment of common machine learning (ML) models; Data visualization using software (Tableau, Power Bi); Formal training/professional experience using relational
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candidate. (1) Develop multisource, frugal downscaling approaches. Most downscaling approaches presented in the scientific literature are Machine Learning (ML)-based. The proposing team's experience is that
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format). Ensure content aligns with industry expectations and supports multiple learning models (self-paced, instructor-facilitated, and short-format non-credit courses). Position Status Part Time Posting
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axes: AI-driven territorial diagnostics and foresight, integrating multi-source satellite data with machine learning and spatial modeling Climate–water–energy–agriculture interactions, with applications
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based on the new data generated, incorporating key variables identified in (i), and use statistical and machine learning methodologies to ensure high predictive accuracy and robustness; iii) validation
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to contribute to the development of innovative machine learning solutions using deep learning and multimodal foundation models. Working closely with leading researchers, you will design, develop, and implement
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: multilevel models for longitudinal EMA data, extraction of characteristics/features from physiological data (signal processing), as well as modeling in machine learning. # Data Management and Structuring
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measurement technique development, atmospheric modelling, and advanced methods for integrating observational and model data through data assimilation and machine learning. About the research project The overall