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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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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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learning pipelines for multilayer segmentation and nanoscale transistor classification from microscopy images; (b) Designing graph-based inference models capable of reconstructing higher-level logical blocks
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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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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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unit: Institute of Computational Perception Entry date: 10/01/2026 Job Reference Number: 34400-2026-002088 Job Duties: Conduct independent research in the fields of recommender systems, user modeling
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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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Scholarship of Teaching and Learning (SoTL) Laboratory to design, build, and maintain a next-generation learning platform that leverages large language models, AI agents, and cloud technologies to support
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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
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that is streamlined, thoughtful, and trusted. We envision a model where individuals think beyond their roles, teams are fully engaged, and the organization takes shared ownership of outcomes, challenging