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Post-Doctoral Associate in the Center for Interdisciplinary Data Science and Artificial Intelligence
of reinforcement learning and bandit algorithms Mathematical and algorithmic perspectives on large language models Statistical learning theory and complexity analysis Automated theorem proving and formal methods
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element modeling, and multi-scale biomechanics is most important, while experience applying these methods to cardiovascular problems is a plus. Required qualifications A PhD in mechanical engineering
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following: quantum computing, quantum optimisation, digital twins, infrastructure modelling, mathematical optimisation, hybrid quantum-classical modelling, simulation, probabilistic modelling, infrastructure
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should hold a PhD (or be close to completion) in a relevant analytical and quantitative discipline, such as civil or transport engineering, transport modelling, mathematics, statistics, data science
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Potsdam-Institut für Klimafolgenforschung e.V. (PIK) | Potsdam, Brandenburg | Germany | about 2 months ago
) in a relevant field, such as economics, geography, climate sciences, physics or mathematics experience in quantitative research, documented by relevant publications in peer-reviewed journals (for PhD
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internationally) Requirements: The successful candidate should have the following qualifications: • PhD in Epidemiology, Data Science, Applied Mathematics, Computer Science, Statistics, Public Health, Economics or
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will be hosted by the School of Mathematical Sciences in UCC. This role is part of a national Centre, Rinn Artificial Intelligence – Research & Innovation in Data Science and AI, a multi-institutional
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In collaboration with Utrecht University, you will quantify fire dynamics and its impacts on vegetation and climate at regional scales, combining global vegetation models with state-of-the-art
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at the weekly seminars. Present results internally and at international venues; Qualifications PhD in Applied Mathematics or Computer Science (at the time of appointment), with strong research record in privacy
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. The tasks include battery cell characterization and modelling based on laboratory tests, and development of algorithms for estimating the charge level, health, and power capability which includes robustness