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Field
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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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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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outcomes ●casual representation learning for real-world data ● deep learning interpretation, fairness and robustness ●Regularly conduct computational experiments to execute algorithms on various health and
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Research, and Meta. Responsibilities: The Postdoctoral Fellows will be responsible for leading ongoing innovative research projects. Examples include: The development of probabilistic deep learning models
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In this role, you will be responsible of developing cutting-edge deep learning models for real-time image and video analysis (e.g., segmentation, object tracking, reinforcement learning), with
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data to design robust, efficient deep learning algorithms that maximize the information extracted from images and delivered to the robot. To be successful in this role, we are looking for candidates
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-dose IL-2 affects the neuro-inflammatory process. You'll use advanced cellular immunology techniques to conduct deep phenotypic and functional characterisation of regulatory T cells (Tregs), establishing
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opportunities, access to modern GPU clusters for deep learning research, and strong academic-industry connections. CADIA's commitment to open science aligns perfectly with this project's goals of creating
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Université Grenoble Alpes, laboratoire TIMC, équipe GMCAO | Grenoble, Rhone Alpes | France | 5 days ago
with expertise in medical image processing—particularly registration and segmentation—and proven experience in deep learning, with a focus on ultrasound imaging. Prostate cancer diagnosis relies
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. You'll use advanced cellular immunology techniques to conduct deep phenotypic and functional characterisation of regulatory T cells (Tregs), establishing their relationship with treatment responses and