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Field
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Lightweight and flexible solar cells Space deployable structures Device analysis in space environments Big data, AI, and machine learning for space solar initiatives Outstanding Postdoctoral Training Strategy
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highly interdisciplinary, integrating big data analysis, state-of-the-art machine learning models, mathematical modeling, and systems biology to elucidate the mechanisms of drug interactions in complex
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analysed by bespoke machine-learning driven algorithms, combined with physical models, to de-noise images, identify features and correlate properties, giving critical insights into power loss pathways
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2025. We seek to recruit a Research Associate specialising in statistical modelling and machine learning to join our multi-university multi-disciplinary team developing a groundbreaking technique based
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Description Conduct a part of the ANR MetaTime (setting-up experiments, acquisition and processing of data, writing scientific reports) • Perform a review of the existing litterature on the topics • Acquire
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equations, uncertainty quantification, and machine learning. Candidates must have obtained a Ph.D. in mathematics or applied mathematics before the start date and must demonstrate research excellence, strong
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acquisition and management. This role directly builds upon Ph.D. research by applying advanced 3D imaging, algorithm design, machine learning, software engineering and visualization techniques to a real-world
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. Job description: - first-principle modeling and simulations of electrolytes - development of new machine learning strategies and quantum simulation approaches - application of specially developed
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Python, MATLAB, or C++. Experience with machine learning techniques, CAD, computational modeling, 3D printing, motion capture, and/or material testing. Proficiency in programming languages commonly used in
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modelling, control, and optimization, with applications in chemical and pharmaceutical manufacturing; data-driven modelling and machine learning applications in process industries; advanced process control