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of generative AI tools, use of large language models, machine learning, and ethical frameworks for AI implementation. Ability to apply AI to interdisciplinary research or developing AI models
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microscopy and cryo-electron tomography to pursue structural studies of the transport machinery. For more information about Dr. Samara Reck-Peterson and the lab, please visit: https://reckpetersonlab.org
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candidate will have recently completed (or be close to completing) a PhD in Computer Science, Machine Learning, Natural Language Processing (NLP), or a related field, with a thesis focused on AI, specifically
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archaeological and historical contexts is also required. Additionally, the ability to perform *ad hoc* data processing (multivariate statistics, machine learning, etc.) is desirable. Proficiency in programming
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, implement and benchmark machine learning models for large-scale health datasets consisting of diverse information including structured medical history, demographics, clinical notes, laboratory measurements
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Inria, the French national research institute for the digital sciences | Sophia Antipolis, Provence Alpes Cote d Azur | France | 3 months ago
Website https://jobs.inria.fr/public/classic/en/offres/2026-10248 Requirements Skills/Qualifications Expertise in computer graphics and AI, possibly including physical simulation and PDEs. Knowledge of C/C
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machine learning-based model to map satellite retrievals to ground based air pollutant concentrations Conducting error assessment on the derived concentration data Implementing new observational data
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 6 days ago
/Mental Requirements Primarily sedentary office work involving continuous sitting, talking, hearing, and computer use, with frequent standing, movement across campus, and facilitation of in-person
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modelling and numerical relativity. The group comprises PI Alicia Sintes, a further ten faculty members and a large number of post-doctoral researchers and PhD students. The group is a member of the IAC3
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learning-based surrogates for physical systems LLMs and scientific agents – large language models that autonomously reason, plan and execute scientific workflows AI for engineering design – LLM-driven agents