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will be adapted to the candidate’s background and the evolving needs of the center. Possible directions include the application of rock physics models, Bayesian inversion methods, and machine learning
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physics models, Bayesian inversion methods, and machine learning algorithms in the electromagnetic context. Qualifications and personal qualities: Applicants must hold a master’s degree (or equivalent) in
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properties of the Higgs boson. The group focuses on final states containing several tau-leptons. The analysis activity is now extended to include generic anomaly searches using Machine Learning. Furthermore
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anomaly searches using Machine Learning. Furthermore, the group takes part in ATLAS upgrade, with participation in the ITk-Pixels project, with responsibilities concerning testing and delivery of pixel
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written and oral English. Experience from one or several of the following areas is an advantage: Programming, image processing and machine learning. Magnetic Resonance Imaging. Laboratory experience from
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of the following areas is an advantage: Modelling and simulations of flow in porous media. Programming, image processing and machine learning Personal and relational qualities will be emphasized. Motivation
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machine learning. Magnetic Resonance Imaging. Laboratory experience from porous media research related to physics and/or chemistry. Personal and relational qualities will be emphasized. Motivation
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machine learning Personal and relational qualities will be emphasized. Motivation, ambitions and potential will also count when evaluating the candidates. Special requirements for the position
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and the national infrastructure NorHTE, which is under establishment, to optimize synthetic steps (and routes) using machine learning in closed-loop (autonomous) optimizations and for parallel synthesis
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flexibility. We plan to use the departments laboratory for automated chemistry and the national infrastructure NorHTE, which is under establishment, to optimize synthetic steps (and routes) using machine