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Field
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contract is based on § 2 WissZeitVG. Your Tasks: Generative diffusion models (DMs) learn to reverse a diffusion process from an analytically known prior distribution to a target distribution that is inferred
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-driven AI models that capture the underlying process–structure–property relationships governing metal additive manufacturing. By combining mechanistic modelling, in-situ sensing, and machine learning
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | about 1 month ago
to intertwine a multi-contact whole-body controller, a digital simulation of the interacting humans, and machine learning models to predict and respond to human movements and intentions. In a crescendo of
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Curious about opportunities for learning, creating, discovering, and innovating, and are encouraged to learn from failure. Show Your Fire by joining our team and exhibiting your passion and pride in your
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driving reactivation of meiotic genes in cancer Assess their impact on cell physiology, genome stability, and chemotherapy response Explore potential therapeutic vulnerabilities that could improve cancer
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at the Faculty of Science and Technology . The PhD position is for a period of 3 years, with a desired start date of 1st of September 2026. The Robotics Group at NMBU works in two key areas: (1) mobile robotics
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degree in Biomedical Sciences, Molecular Biology, Physiology, or a related field. Affinity with stem cell and cardiac biology – You have experience with, or a strong desire to learn, hiPSC culture
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mononuclear cell (PBMC) analysis and RNAseq, as well as whole slide images (WSI) and immunohistochemistry images on an OMERO online repository. With funding from Cancer Research Horizons, the data is being
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Amsterdam Rotterdam); teach seminars and/or practical for bachelor and master students; supervise bachelor and master student projects; present data at local, national and international scientific meetings
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. While autonomy is becoming more integrated into modern mobility, the reliability of Position, Navigation and Timing (PNT) systems—especially in environments where GNSS signals are denied or degraded