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& Solar Power Plants Battery Energy Storage Systems (BESS) Smart Grids & Power Systems Sustainable buildings, communities and cities[EC1.1] Artificial Intelligence & Machine Learning for Energy Digital
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 10 days ago
Website https://jobs.inria.fr/public/classic/en/offres/2026-09928 Requirements Skills/Qualifications Profile: - The candidate is completing a Master's or engineering’s degree in Computer Vision, Electrical
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machine learning approaches Collaborating with experimental and clinical research partners Support and preparation of scientific reports and journal articles
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; - academic curriculum with background on the computer science and machine learning background; - previous research and professional experience on the scientific domains of the work. Additional Information
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stability analysis and control, machine learning, dimensionality reduction and high-performance computing. Where to apply Website https://emploi.cnrs.fr/Offres/Doctorant/UPR3346-NADMAA-159/Default.aspx
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is looking for an aspiring PhD candidate to research causal machine learning and uncertainty quantification for Earth Observation time-series. Currently, predictive AI in Earth Sciences relies heavily
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remains challenging, limiting multiscale imaging approaches in near-field holotomography. To address this, the PhD project combines machine learning, high-performance computing, and synchrotron-radiation
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systems, statistical physics and machine learning, and using these insights to develop new methods, with the support of competent and friendly colleagues in an international environment? Are you looking
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the "Machine Learning and Gene Regulation" team led by William Ritchie, specializing in bioinformatics and post-transcriptional regulation. The scientific environment at the IGH — international seminars, journal
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: Alexandre José Malheiro Bernardino (ist13761) Organic Unit: Scientific Area of Systems, Decision and Control Scholarship Theme: Computational Auditory System Simulators and Machine Learning-based Optimisation