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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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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 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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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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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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requirement Experience with data analysis and machine learning models is an advantage Experience from molecular modelling or molecular dynamics simulations is an advantage Applicants must be able to work
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employment that the master's degree has been awarded. Experience from protein bioinformatics is a requirement Good programming skills are a requirement Experience with data analysis and machine learning models
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important for renewable energy production and production variability will be an advantage. Knowledge of machine learning or optimization will be an advantage. Applicants must be able to work independently and
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UNIX/Linux interface and basic programming (e.g. Python) is a requirement. Experience with machine learning is an advantage. Experience from free energy calculations is an advantage. Applicants must be