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probabilistic modelling Familiarity with stochastic processes (e.g., Markov chains, stochastic differential equations) Prior exposure with Transformer architectures or large-scale sequence modelling Prior
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planetary systems. The project will investigate Probabilistic Numerical Computation for large scale Inverse Problems, with a particular focus on cases governed by Partial Differential Equations (PDEs). Recent
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– derivatives, wave functions, linear algebra, differential equations, numerical optimization. Some background in solid-state physics, optics, electrical engineering, chemistry, and/or materials science
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oral and written presentation skills in English equivalent to level B2 . Knowledge of applied mathematical modeling, in particular numerical solution of partial differential equations. Experience with
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differential equations and boundary conditions describing heat and fluid flow will be embedded directly into the learning process to constrain model training and reduce data requirements. Time- and space
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within the framework described above. Academic publications and popular science dissemination. Contribute in the SURE-AI project activities. Participate in the Differential Equations and Numerical Analysis
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into the following sections: A - Algebra, Number Theory and Logic B - Analysis and Differential Equations C - Discrete Mathematics D - Geometry and Topology E - Numerical Mathematics and Scientific Computing F
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needs JavaScript for all functions to work properly. Please turn on JavaScript in your browser and try again. UiO/Anders Lien 5th March 2026 Languages English English English Join PROMENTA Research Center
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, differential equations, geometry/topology, numerical analysis, optimization, and statistics. Part of the research is also carried out in close cooperation with other fields of science and technology at NTNU, as