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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
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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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Postdoc Impact of Computational Infrastructures on Public Institutions and Administration of Justice
public institutions in the justice sector. The project will seek to understand how software production -- the practices, economic models and material conditions necessary to deliver digital services
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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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-derived organoid models. You will work closely with in-house technology platforms, including the Single Cell Genomics Facility, Big Data Core and High Throughput Screening Facility. Our research is embedded
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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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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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Location ESTEC, Noordwijk, Netherlands Our team and mission Internal Research Fellow (PostDoc) in Digital Twin Spacecraft and Simulation in the Modelling and Simulation Excellence Section, Future