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Field
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at the interface of probability theory and artificial intelligence, whose goal is to rethink and structure the way mathematical research is produced in the era of AI. Key Responsibilities: Designing and implementing
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, thoroughness, and a structured approach to problem-solving. Strong foundations in linear algebra, probability theory, and calculus are required. Solid skills in programming and machine learning are required
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vision), and the division of statistics and machine learning at the department of computer and information science (focusing on the theory behind machine learning). The employment When taking up the post
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of theory, which will greatly facilitate the subsequent benchmarks, especially at the TD-DFT level. Thanks to this, the PhD candidate will perform extensive electronic structure benchmarks considering a wide
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for the first four full-time equivalent years of your doctoral studies. You will have the opportunity to work with leading national and international researchers – experts in social network theory, qualitative
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of calculations on a hardware architecture that matches the inherent nature of quantum chemistry electronic structure calculations and with it the opportunity to capture some of the inherent physics, albeit with
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Simulation Group at ICN2 conducts cutting-edge research in computational materials science, focusing on electronic structure methods, atomistic simulations, and multiscale modelling. The group develops and
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of Science and Technology (NTNU) for general criteria for the position. Preferred selection criteria Strong computational skills Background in structural reliability theory or probabilistic methods Knowledge
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of research advised by one of the MPIM directors: Arithmetic Geometry (Scholze) Geometric Langlands Theory (Gaitsgory) Topology (Teichner) Further research directions are available with other IMPRS board
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workflow that maps first-principles electronic-structure data onto predictive atomistic spin-Hamiltonians and device-scale dynamical models. The candidate will run high-throughput, relativistic DFT