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Field
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efficient for medicine? If the answer is yes, please continue reading! Join our team! We are looking for a PhD student to work on the topic of shape analysis for medical imaging, tailored for deep learning
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analysis; proven experience with mathematical/computational modeling of experimental observations; experience with colloidal synthesis; proven experience with optical and fluorescence microscopy techniques
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will be secondary data analysis, linking reported incidents of violence with weather data to identify correlations between climate variables (e.g., temperature extremes, humidity) and violent crime
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from a technical and scientific perspective. The Ph.D. candidate will develop methods for uncovering DNS infrastructure dependencies at scale, relying on active measurements and graph-based analysis
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interdisciplinary character. The Faculty of Science, Technology and Medicine (FSTM) at the University of Luxembourg contributes multidisciplinary expertise in the fields of Mathematics, Physics, Engineering
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quantitative skills and strong analytical capabilities; proven experience with experimental image and data analysis; proven experience with mathematical/computational modeling of experimental observations
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on the use of new Lyapunov-based deep learning methods. Such development includes: ideation, mathematical development, Lyapunov-based analysis, executing simulations and experiments, and disseminating research
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, Master’s, and Doctoral degree programs) closely linked to both primary and applied research and high school teaching of the following sciences: Mathematics, Physics, Chemistry, Biology, and Earth sciences
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, mathematics, physics, remote sensing and machine learning. Experience and skills · Strong interest in modelling, model-data integration, and remote sensing data analysis. · Knowledge of programming, remote
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data, and primarily focus on developing medical image analysis and AI methodologies. The project is a collaboration with Radboud UMC, ProBCI (Dutch Bladder Cancer Infrastructure), and IKNL (Netherlands