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- Université Grenoble Alpes, laboratoire TIMC, équipe GMCAO
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25 Aug 2025 Job Information Organisation/Company Institute of Nuclear Physics Polish Academy of Sciences Department Department of Magnetic Resonance Imaging /NZ56/ Research Field Physics Researcher
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mechanics and 3D imaging at the division of Solid Mechanics. You will also be part of the strong international research environment at MAXIV and will work closely with users of the facility helping them
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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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learning Deep learning model generalisation techniques Translating deep learning models into clinical settings Experience developing deep learning models for real-time image/video segmentation, object
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Université Grenoble Alpes, laboratoire TIMC, équipe GMCAO | Grenoble, Rhone Alpes | France | about 20 hours 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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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 this role, you will
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settings Experience developing deep learning models for real-time image/video segmentation, object tracking, 3D reconstruction, super-resolution. Have a passion on obtaining external funding and project
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Job Description Apply now Job Title: Postdoctoral Associate Division: Neuroscience Work Arrangement: Onsite only Location: Houston, TX Salary Range: Hiring upto $63,200 FLSA Status: Exempt Work
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The Nanoscale Magnetic and Electronic Heterostructure Group of the Materials Science Division (MSD) invites applications for a Postdoctoral position focused on Lorentz Transmission Electron
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About us: We are seeking experts in medical image deep learning to join our team and help develop novel computationally efficient segmentation algorithms. We welcome application from individual with