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track record proven by relevant experience, publications, etc. The applicant is expected to have: MSc degree in Computer Science, Artificial Intelligence, Electrical Engineering, Applied Mathematics
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, you will also work on analysing the theoretical properties of these algorithms using the mathematical framework of randomised complexity theory. You will be part of both the Probabilistic Graphical
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consistency models. These models rest on a joint foundation of deep learning architectural research and mathematical theory. They therefore present an exciting research area for theoretically minded individuals
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? Then come join us as our next PhD-candidate in the Uncertainty in AI group! Irène Curie Fellowship No Department(s) Mathematics and Computer Science Reference number V32.7482 Job description The
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combination of mechanism-based mathematical models to describe drug concentrations and effects, and statistical models such as nonlinear mixed effect models to quantify and predict variability between patients
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probabilities Robust stochastic processes Tractable models and decision-making Online/continual learning with evolving data Requirements Specific Requirements Master's degree in Mathematics, Statistics
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composite materials and structures. Good mathematical skills and experience in numercial methods for structures and/or fluids. Good programming skills (e.g. Matlab, Python). You have obtained an MSc
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come from a range of backgrounds including, but not limited to, Physics, Atmospheric Science and Geoscience, Computational Science and Applied Mathematics. You should have practical programming
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of the lab's research direction. Requirements Specific Requirements We are looking for a creative and enthusiastic researcher with an MSc degree in Computer Science, Mathematics, Physics, Engineering or a
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or equivalent degree in computer science, mathematics or a closely related field, or expect to obtain such a degree soon. You are able to work both independently and as part of a team. You are proficient in