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assessment. You will be provided with access to various engineering and computation toolsets along with the high-performance computer. A good background in numerical methods and computational platforms is
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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
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through our Terms of Employment Options Model. In this way, we encourage our employees to continue to invest in their growth. For more information, please visit Working at Utrecht University external link
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work hands-on with clinical data and build robust deep learning algorithms. We welcome applications from individuals with experience in: Experience developing deep learning models for real-time image
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. actively contributing to the preparation of other strategy/decision papers and reports, as well as information notes or other reporting media. preparing training materials and providing training within
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reality and mixed reality; data modelling and databases, in particular the spacecraft reference database; semantic modelling in support of digital continuity and semantic interoperability; digital twin
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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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current research/activities Ability to gather and share relevant information General interest in space and space research Behavioural competencies Education You should have recently completed, or be
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to derive overarching scaling principles that apply across multiple disciplines, including hydrology, socioeconomics, toxicology and ecology. To achieve this, you will collect data from databases, reviews
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are as follows: Estimate the onshore technical and economic potential for CCUS in rural areas in Germany, the Netherlands and Norway. Gather high resolution spatial explicit data on (potential) CO2 sinks