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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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of Mathematics at the Hebrew University of Jerusalem is soliciting applications for postdoctoral positions, to be hosted by one of the faculty members listed below. Candidates must hold a PhD in mathematics, or
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on "Canalization and Other Design Principles of Gene Regulatory Network Models" (https://www.nsf.gov/awardsearch/showAward?AWD_ID=2451973 ) and "A mathematical theory for the biological concept of modularity” (https
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Inria, the French national research institute for the digital sciences | Pau, Aquitaine | France | 2 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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., 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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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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model predictions Writing of the thesis and publication about the relation of experimental data and model results Your Profile: A Masters degree with a strong academic background in physics, mathematics
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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