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assessment of seed yield and quality traits using imaging, machine learning, and edge computing technologies. The position will assist with the development, testing, and deployment of AI models for analyzing
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support the development of AI-driven and data-driven approaches for the discovery and design of functional materials. The role will involve the development and application of machine learning models, high
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recognized as a national model for civically engaged urban universities and was selected by the Carnegie Foundation for the Advancement of Teaching for a Community Engagement classification. Washington Monthly
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of mathematical modeling, with a particular focus on stochastic modeling, optimization and more recently machine learning. Indeed, over the last years, the team’s activity has been marked by a strong shift toward
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following areas: mathematical optimisation, vehicle routing and scheduling, machine learning, reinforcement learning, logistics simulation, predictive analytics, or transport modelling. Strong programming
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analysis; (b) machine learning-programming and algorithmic code development; (c) literature review and data search; (d) computational modeling and numerical simulations; (e) literature review and data search
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analysis, computer vision, or machine learning, with a clear interest in developing image analysis algorithms and an affinity with medical topics. Good communication and organizational skills are essential
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methods You will work closely with: - Dr. Martin Ramacher (machine learning for environmental applications) - Dr. Matthias Karl (urban air quality modelling and emissions) and collaborate within a project
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | about 2 months ago
-tier machine learning and computer vision venues, actively participating in departmental seminars, and contributing to collaborative projects. Where to apply Website https://jobs.inria.fr/public/classic
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, machine learning, data-driven dynamics, stability/perturbation theory/rigorous numerics. Possible applications of the new theory include the analysis of spatiotemporal data arising from weather and climate