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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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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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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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Translating deep learning models into clinical settings Experience developing deep learning models for real-time image/video segmentation, object tracking, 3D reconstruction, super-resolution. Have a passion on
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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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-up. In the ZonMW-funded AI for EVAR project, we develop multi-modal models for optimized selection of treatment before, and follow-up after EVAR. You will implement and advance multimodal deep learning
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into the transport and storage of hydrogen, while driving further development. Comprising thirty-two research and industry partners, HyTROS seeks to fast-track the scaling up of green hydrogen in the Netherlands
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thirty-two research and industry partners, HyTROS seeks to fast-track the scaling up of green hydrogen in the Netherlands. For the establishment of a strong and safe hydrogen infrastructure, it is
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improve both imaging quality and treatment precision in rodent models, with the ultimate aim of translating findings into clinical trials. This project has two main objectives: Developing novel hardware and