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than 18,500 people, including over 14,000 students and 4,000 researchers from more than 120 different countries. Post-doc on formal verification and algorithm discovery for numerical analysis About us and our
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than 18,500 people, including over 14,000 students and 4,000 researchers from more than 120 different countries. Postdoctoral Position in Computational Genomics, Machine Learning & Single-Cell Biology Mission
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for Transportation (VITA ) is looking for a postdoctoral researcher in the area of Generative AI. VITA research interests lie at the intersection of Computer Vision, Machine Learning (Deep Learning), and Human-Robot
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on developing new generative modeling approaches, scalable training algorithms, and foundation model technologies. The role is suited for candidates with a strong machine learning background who are excited
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looking for a postdoctoral researcher in the area of Generative AI. VITA research interests lie at the intersection of Computer Vision, Machine Learning (Deep Learning), and Human-Robot Interaction
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of sensorimotor processing. We are recruiting two postdocs: Postdoc in Computer Vision & AI for Behavior Analysis Postdoc in Embodied AI (Reinforcement Learning for Motor Control) Main duties and responsibilities
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: A doctorate in a Machine-Learning related field A deep knowledge of Control Theory, both classical and deep learning based A solid publication record in top level ML venues such as NeurIPs, ICML, and
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and in computer architecture domains, our ability to analyze data still falls behind the unstoppable data collection rates. Data-intensive applications are increasingly more demanding in sophisticated
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Understanding human motivation requires methods that go beyond questionnaires and simplified computer-based tasks. This project aims to develop more naturalistic, yet highly controlled, behavioral assays in which
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and associated environmental impacts. Contribute to short-term (2026 to 2030) and long-term (2030 to 2050) verticalisation forecasting models based on machine learning, and to their validation against