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Sorbonne Université SIS (Sciences, Ingénierie, Santé) | Palaiseau, le de France | France | about 2 months ago
appelé Cloud-Resolving Model (CRM). Il s'agit d'un modèle atmosphérique non hydrostatique simulant un domaine local relativement petit (quelques centaines de kilomètres, comparé au rayon de Jupiter de 70
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execution models towards designing the next-generation unified cloud stack. CloudNG has a strong emphasis on performance and performance predictability, sustainability, seamless accelerator integration, and
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-temporal machine learning method development, including: generative models for grid-based and particle-based spatio-temporal data; controlled generation methods for data assimilation; and graph-based multi
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efficiency and reliability. Strong background in data analytics, leveraging insights to drive operational improvements and predictive maintenance. Experience in control strategies and automation, ensuring
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, project and program evaluation, and report writing. Data science and Geospatial Analysis skills, including coding (e.g., Python, R), inferential statistics (e.g., MATLAB, STATA), predictive modeling, GIS
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quantitative predictions testable against empirical data from diverse ecological contexts. We use methods from theoretical evolutionary biology, including optimal control theory, life history modelling, adaptive
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Join us for an exciting Doctoral student journey that will combine systems biology, computational modeling, and industrial biotechnology to solve a key challenge in sustainable biomanufacturing
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sequencing, and predictive modelling to define the immediate molecular consequences of light and temperature signals. One crucial component of plants’ sensory network is the circadian clock. In plants
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Sharing – Building a federated data space to enable responsible data integration and cross-project learning. AI & Modelling – Using shared data to power advanced models that help describe and predict
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prediction, focusing on efficient edge deployment (e.g., through model pruning, quantization, or TinyML techniques). The embedded system will be designed to perform local inference in real-time, minimizing