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contribute to the development and implementation of cutting-edge AI solutions for real-time image-guided medical applications. You will work hands-on with clinical data and build robust deep learning
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publications based on your PhD research. An interest in contributing to the clinical translation of preclinical research innovations. More information About GROW Research Institute for Oncology and Reproduction
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eager to apply your skills to translational oncology research. You also have: A recent PhD in (bio)medical physics or (bio)medical engineering. A strong interest in radiotherapy, CT imaging, dosimetry
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partners to reduce CO2 emissions in steel production using machine learning. You can find more information here . You will work on a theoretical and an applied project on data-enhanced physical reduced order
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. You have a background in machine learning for spatial data (e.g., random forest, neural networks) or are open acquiring these skills. You have experience with handling large geospatial datasets and
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, such as R, Python, or Machine Learning, to identify patterns in biological factors, disease and mortality; co-supervising and mentoring PhD candidates, MSc and BSc students; collaborating with national and
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. The team consists of four scientists Monique van der Veen, David Vermaas, Ruud Kortlever and H.Burak Eral collaborating in the context of Pro2Tech institute. Given the rapid development and large interest in
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. You have a background in machine learning for spatial data (e.g., random forest, neural networks) or are open acquiring these skills. You have experience with handling large geospatial datasets and
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in this kind of environment will result in models too large to be handled and too instable to be solved. Data-driven approaches need to be used in addition to enrich the physics-based models
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30 Aug 2025 Job Information Organisation/Company Delft University of Technology (TU Delft) Research Field Engineering » Chemical engineering Engineering » Mechanical engineering Researcher Profile