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to develop strong skills in viscous fluid mechanics, numerical methods and applied mathematics. Start date: October 2026. Duration: 4 years. Education: A strong 4-year degree or MSc degree in Mechanical
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look forward to receiving your application! Do you have a background in machine learning and interested in telecommunications? You have a chance to contribute to development of sensing methods for new
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application! Do you have a background in machine learning and interested in telecommunications? You have a chance to contribute to development of sensing methods for new distributed MIMO systems. Your work
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edge of energy systems and computational engineering, developing scalable methods to simulate and secure IBR-dominated grids. Your key responsibilities include: Conducting large-scale simulations
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instruments, data analysis as well as theoretical and numerical modelling provide excellent research opportunities for PhD students. Solar system science research subjects include cosmochemistry, small bodies
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medicine and numerous accredited Bachelor's and Master's degree programs. MSB is part of the IRO Group, which also includes MSH Medical School Hamburg, HMU Health and Medical University Erfurt, HMU Health
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EuroHPC (Arrhenius) and an AI Factory (Mimer). Cybersecurity research at LiU focuses on securing the most critical functions of digital society. Through numerous national and European projects, methods
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) analysis on gold surfaces. The student will develop a data analysis method (e.g. statistical methods) to identify the main parameters driving metabolite-surface interactions in the presence of different
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synaptic resilience and the reliability of synaptic responses. The work primarily involves mathematical modeling and numerical simulation, but also the analysis of experimental datasets for model validation
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 2 months ago
solving complex inverse problems that link measurements to their underlying causes. This PhD interdisciplinary programme focuses on Bayesian methods for estimating physical parameters from high-dimensional