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longitudinal, personalised fracture-risk trajectories using repeat imaging. The successful candidate will therefore be working primarily as an AI/image-processing researcher, developing deep-learning models
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implementing, training, and evaluating machine-learning or deep-learning models. You are not expected to already be an expert in both areas, You are genuinely motivated to develop expertise in the
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Computational Fluid Dynamics (CFD), fluid mechanics, and Artificial Intelligence (AI), with a particular focus on developing deep reinforcement learning methods for active flow control of hydraulic
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topics: Development of deep learning models Mathematical foundations of machine learning Optimal deployment of AI systems The candidate is expected to hold a relevant MSc degree in Computer Science
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language processing. Where to apply Website https://emploi.cnrs.fr/Offres/Doctorant/UMR8554-YAILAK-003/Default.aspx Requirements Research Field Language sciences Education Level PhD or equivalent Research
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fellow salary, which is determined by the number of years post-PhD, and benefits can be found at https://postdoc.hms.harvard.edu/guidelines . With this appointment, you are represented by the Harvard
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knowledge Applicants should have: A good background in machine learning, deep learning, artificial intelligence, or computer vision Strong Python programming skills Experience with at least one deep
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machine learning, deep learning, foundation models/LLM. Excellent quantitative and analytical skills. Strong written and interpersonal communication skills. Strong motivation & ability to work
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posts Starting date: 14.08.2026 Job description: Postdoctoral Researcher - Probabilistic Deep Learning for Urban Air Quality (AEON-UP) Referenzcode: 1056 Arbeitsort: Geesthacht Bewerbungsfrist: 03.09.2026
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initiative focused on AI-assisted reverse engineering of integrated circuits for hardware assurance and intelligence analysis. The project is conducted within the Deep Learning for Perception and Data