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/or FE simulation results and modern machine learning methods may be evaluated for this purpose. The current project is relevant for a wide range of academic and engineering disciplines, including
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create statistically significant data. Use the measurement data to improve digital models of the structure and the load process for relevant excitation, such as traffic and wind. Explore machine learning
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PDEs and equations subject to stochastic perturbations, integrating approaches from machine learning algorithms, transport theory, and optimization. Examples of relevant equations include, but are not
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, mathematics (Operations research) or Computer Science or Machine Learning) the master thesis must be included in the application Ideal Candidate: demonstrates experience or strong interest in modelling
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, with interests spanning a broad range of areas - including statistical machine learning, high-dimensional data and big data, computationally intensive inference for complex models, causal inference and
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at https://cbu.w.uib.no/joshi-group/ . Co-supervisors include experts in machine learning and AI, Pekka Parviainen and Tom Michoel, alongside leading epidemiologists, Tone Bjørge and Kari Klungsøyr. The core
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Science) in a uniquely interdisciplinary environment. The project will be supervised primarily by Professor Anagha Madhusudan Joshi-Michoel, who specializes in applying machine learning and data science
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of cancer; probabilistic modelling and Bayesian inference, stochastic algorithms and simulation-based inference; and statistical machine learning. More about the position The position is part of the project
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of machine learning (ML) techniques and density functional theory (DFT) simulations as well as experimental validation. For more information and how to apply: https://www.jobbnorge.no/en/available-jobs/job
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and aerodynamic loads. The project will be co-supervised by experts in aeroelasticity and machine learning and can include aspects of fluid-structure interaction and digital twins depending