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energy efficiency while keeping the grid reliable and secure. Our research method is engineering-oriented, prototype-driven, and highly interdisciplinary. Our typical research process includes
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to 2 years; an extension of an additional 2 years is possible. TASKS: Mathematical and physical modeling to determine greenhouse gas and pollutant emissions in cities using novel atmospheric measurements
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, computer science, mathematics, physics, or a related field with an outstanding academic record. Interest in mathematical signal processing, optimization, and/or machine learning is important. Since
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. Your qualifications ▪ Above-average university degree in electrical engineering, communications engineering, mathematics, physics (or similar) with thorough knowledge in quantum information and
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. Your qualifications An excellent PhD degree either in Computer Science, Physics, Mathematics or related fields, ideally with a background in quantum theory, quantum computing or quantum machine learning
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. Requirements: Completed university degree in computer science or applied mathematics, remote sensing, geophysics, physics, or related areas Expertise in computer vision and/or machine learning (deep learning
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, christian.deppe@tum.de) Your qualifications ▪ Master’s or doctoral degree in electrical engineering, communications engineering, mathematics, physics (or similar) ▪ Interest in theoretical work with high practical
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) and present your work at top conferences and journals in our field. Candidates should have completed their Master/Diploma studies in Computer Science, Mathematics, Mechatronics, Electrical Engineering