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recognition neural networks (e.g., Tsetlin Machines, Graph Neural Networks, RNNs for time-series causal discovery, XGBoost). AI-driven heuristic search (e.g., genetic algorithms, genetic programming, simulated
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and application of fast solvers for Maxwell’s equations and nonlinear inversion algorithms that we have already developed in a previous PhD project. In addition to electromagnetic geophysics
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electromagnetic data during drilling. This includes the further development and application of fast solvers for Maxwell’s equations and nonlinear inversion algorithms that we have already developed in a previous
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and Cell Identity, relevant for stem cell biology and cancer. The position will be within the Molecular Signaling and Bioenergetics group headed by Prof. Mathias Ziegler. About the project/work tasks
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. General information: For further details about the position, please contact Prof. Geir Ersland, Department of Physics and Technology, e-mail: Geir.Ersland@uib.no or Prof. Kundan Kumar, Department
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appendices with certified translations into English or a Scandinavian language must be uploaded at Jobbnorge. General information: For further details about the position, please contact Prof. Geir Ersland
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/Machine Learning (AI-ML) approaches to meeting this challenge. Possible topics include, but are not limited to: storylines for plausible narratives of regional climate change, novel algorithms for rare
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collaborative skills. Applicants must be proficient in both written and oral English. Experience from one or several of the following areas is an advantage: Developing algorithms for CFD solvers (e.g. OpenFOAM
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for plausible narratives of regional climate change, novel algorithms for rare event sampling or ensemble boosting, and the development and use of hybrid climate models combining physics-based and ML components
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advantage: Developing algorithms for CFD solvers (e.g. OpenFOAM). Programming in C++ or Fortran and proficiency with MATLAB or Python scripting. Experience with tools for simulating chemical kinetic, e.g