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-learning–based segmentation, classification and tracking for microbes and microgels in phase-contrast and fluorescence images Optimise these models and pipelines for real-time performance and integrate them
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, numerical implementation, and analysis of multi-scale heat transfer phenomena. Co-supervise PhD students and contribute to mentoring early-career researchers involved in related modelling activities. Attend
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Mathematics (Inverse Problems), Computer Science (Machine Learning, Computer Vision, Efficient Algorithms and High-Performance Computing), and Physics (Image Formation Modelling). Your project is part of
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., Pettersson, H., Behrens, A., Männik A., 2018. Comparing a 41-year model hindcast with decades of wave measurements from the Baltic Sea. Ocean Engineering, 152, 57–71. https://doi.org/10.1016/j.oceaneng
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Inria, the French national research institute for the digital sciences | Pau, Aquitaine | France | 3 days ago
science as part of the ERC Starting Grant project Incorwave, which aims to develop advanced numerical and mathematical methods for passive seismic imaging. The research will focus on two key-directions: (1
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Mathematics (Inverse Problems), Computer Science (Machine learning, Efficient Algorithms and High-Performance Computing), and Physics (Image Formation Modelling). Your project is part of the NXTGen High-tech
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Research Centre (CRC) 1450 “inSight – Multiscale imaging of organ-specific inflammation” (https://www.uni-muenster.de/CRC-inSight) The project is based in the research group of Prof. Dr. Kerstin Steinbrink
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viewpoints, and the use of disguises. This interdisciplinary PhD offers an exciting opportunity to help modernise eyewitness identification by combining cognitive psychology, immersive technology, and
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This project focuses on the development of quantum–classical modeling strategies for multiphase flow systems. The PhD topic is on exploring how emerging quantum computing methods can be integrated with classical
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trustworthiness of mathematical models and machine learning tools (e.g., neural networks) in a meaningful way, we need innovative, scalable methodologies that efficiently and accurately capture, represent, and