64 modeling-and-simulation-"UNIVERSITY-OF-SOUTHAMPTON" Postdoctoral positions in Netherlands
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- Utrecht University
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- Eindhoven University of Technology (TU/e)
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- University of Twente (UT); Enschede
- Delft University of Technology (TU Delft)
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- Maastricht University (UM); Maastricht
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- Radboud University Medical Center (Radboudumc); Nijmegen
- The Netherlands Cancer Institute
- The Netherlands Cancer Institute; Amsterdam
- Vrije Universiteit Amsterdam (VU); Amsterdam
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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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Applications are invited for a 2-years position as postdoctoral researcher within the research project “Graphs and Ontologies for Literary Evolution Models” (GOLEM), financed by an ERC Starting
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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
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cutting-edge 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
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architectures and patterns for space and ground-segment systems. 2) Digital system security engineering Utilising novel model-based frameworks to facilitate the application of emerging system security standards
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the potential to radically change and improve the performance of breast cancer screening, allowing for an increase in the early detection of cancer, while reducing the rate of false alarms. As a result, we
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, heavily relying on clinician expertise. This project funded by the Hanarth fund combines ultrasound imaging with histopathology data to train advanced AI models for automatic tumor segmentation, enabling
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to localize anomalous sounds, related to faults, in a complex acoustic environment, characterized by moving sound sources and reverberations. Purely relying on physical models describing the acoustics
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interpretation is subjective, heavily relying on clinician expertise. This project funded by the Hanarth fund combines ultrasound imaging with histopathology data to train advanced AI models for automatic tumor