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, aiming to transform the care for patients with abdominal aortic aneurysms (AAA). You will develop and validate cutting-edge multimodal deep learning models that integrate imaging and clinical data
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experimentally calibrated material models and tribological behavior for recycled aluminum alloys. This initiative will provide predictive capability and design guidelines for the downgauging of aluminum trays from
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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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applications from individuals with experience in: Deep learning Medical image computing (preferably x-ray imaging) Computationally efficient deep learning Deep learning model generalisation techniques
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University, you will build models and methods to parse natural language questions into geo-analytical workflows, combining NLP and semantic representations to improve how complex spatial questions can be
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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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availability of data and connectivity through the Internet of Things create new opportunities to incorporate real-time insights into decision-making, combining tractable modelling with provably efficient
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emissions. The EcoTray project aims to develop a validated, high-resolution finite element simulation framework that integrates experimentally calibrated material models and tribological behavior for recycled
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multimodal deep learning models that integrate imaging and clinical data to personalize treatment and follow-up strategies. In the Netherlands, around 75% of patients with an abdominal aortic aneurysm (AAA
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: Deep learning Medical image computing (preferably x-ray imaging) Computationally efficient deep learning Deep learning model generalisation techniques Translating deep learning models into clinical