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flow behavior. The project also involves applying machine learning and computer vision techniques to enhance data analysis, pattern recognition, modeling, and prediction. The role requires a solid
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collaboration with the Materials Design Division (also at IFM) and with the Computer Vision Laboratory at the Department of Electrical Engineering (ISY), a world-class research environment specializing in
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of 550 or more (computer based test 213, internet based 79) Cambridge/Oxford - Advanced or Proficiency level Selection The selection among the eligible candidates will be based on their capacity to benefit
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or more (computer based test 213, internet based 79) Cambridge/Oxford - Advanced or Proficiency level Selection The selection among the eligible candidates will be based on their capacity to benefit from
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none of the sections scoring less than 5.0) TOEFL score of 550 or more (computer based test 213, internet based 79) Cambridge/Oxford - Advanced or Proficiency level Selection The selection among
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score (Academic) of 6.0 or more (with none of the sections scoring less than 5.0) TOEFL score of 550 or more (computer based test 213, internet based 79) Cambridge/Oxford - Advanced or Proficiency level
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or more (computer based test 213, internet based 79) Cambridge/Oxford - Advanced or Proficiency level Selection The selection among the eligible candidates will be based on their capacity to benefit from
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-ray computer tomography. The position will be open at the Fluid Dynamic division of Mechanical and Maritime Science department. The research at the Division covers turbulent flow (both compressible and
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multiphase flow behavior. The project also involves applying machine learning and computer vision techniques to enhance data analysis, pattern recognition, modeling, and prediction. The role requires a solid
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novel machine learning method development. However, you will be part of a larger cross-disciplinary research initiative involving both computer and material scientists, providing excellent opportunities