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our education, while benefitting them with our talent supply and collaborative research achievements. The University’s unique applied learning pedagogy integrates work and study, embedding authentic
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of developing cutting-edge deep learning models for real-time image and video analysis (e.g., segmentation, object tracking, reinforcement learning), with applications to medical imaging and robotic systems. In
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. This unique position combines advanced finite element modeling, machine learning, and experimental studies, while offering the opportunity to contribute to open-source libraries and collaborate directly with an
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uncertainty quantification into scientific machine learning workflows and optimize the design of computational (ABM) and wet-lab experiments. • Collaborate with mathematical modelers and experimentalists in
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approaches and will integrate novel hardware (including electrode arrays, microdevices, analytical systems) into automated robotic pipelines You will also apply machine learning-based analyses to imaging and
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and application of novel AI, machine learning, and statistical methods for biomedical and health data. The candidate will engage in both independent and collaborative research, driving innovative
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implement cutting-edge AI solutions for real-time, image-guided medical applications, with a focus on advanced robotics. You will work directly with clinical data to design robust, efficient deep learning
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to systematically understand cancer biology, identify diagnostic and prognostic biomarkers, and improve cancer therapy. Projects will involve the development of AI solutions, including machine learning, deep learning
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modeling, machine learning, and experimental studies, while offering the opportunity to contribute to open-source libraries and collaborate directly with an innovative startup partner. You will be
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Université Grenoble Alpes, laboratoire TIMC, équipe GMCAO | Grenoble, Rhone Alpes | France | 4 days ago
precision and treatment decisions. Within a collaborative project involving researchers, clinicians, and industry partners, the team has developed innovative deep learning–based segmentation methods and built