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Brandenburg University of Technology Cottbus-Senftenberg • | Cottbus, Brandenburg | Germany | about 13 hours ago
study programme aims to promote qualitative and quantitative research on the protection and use of the heritage of mankind in an international and interdisciplinary context. The focus is on theoretical
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with strong interconnection between experimental and theoretical physics. We provide a large network of collaborators to develop ideas and new projects. Excellent experimental research infrastructure
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. The programme enables the doctoral candidates, the Junior Fellows, not only to write their dissertations within the time to degree but also to enhance their research skills through theoretical and methodological
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Faculty 08: Philosophy and History Faculty 09: Linguistics, Cultures, and Arts Faculty 10: Modern Languages Faculty 11: Geosciences and Geography Faculty 12: Computer Science and Mathematics Faculty 13
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, and Ability Heroes. This research programme is supported by a two-year research fellow post, and $50,000 in research expenses each year over this period. We have an established training programme with
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My interests span a wide range of topics in theoretical physics, including: geometric phases, topological defects in matter and radiation fields, inverse problems (scalar and vector tomography
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apply early for the best opportunity to be considered. How To Apply Apply online by clicking the 'Apply' button, above. Select programme type (Research), 2025/26, Faculty of Engineering and Physical
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I am an experimental particle physicist that works on the search for phenomena that are beyond our current theoretical understanding in terms of the Standard Model of Particle Physics. The research
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the theoretical effort on light-matter coupled systems. I have projects available within the following areas, all of which can be tailored to either honours or PhD level. "Quantum impurities in quantum gases
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contributing to final layer predictions. • Objective 3: Non-convex Optimization and Local Minima -- Study the theoretical foundations and empirical behaviors of deep neural networks in the context of non-convex