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theoretical analysis, implementation of methods in computer codes, use of state-of-the-art high-performance computers in Sweden and in Europe, application of machine-learning and AI techniques, and
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requirements for doctoral studies, you must: hold a Master’s (second-cycle) degree in engineering physics, electrical engineering, machine learning, data science, computer vision, computer science, applied
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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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general. Computer vision can also be included if there is interest. The graduate school within WASP is dedicated to provide the skills needed to analyze, develop, and contribute to the interdisciplinary
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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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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
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: Help develop a non-invasive computer vision method to track and analyze how hens move in 3D space. You will gain hands-on experience in behavioural studies, animal welfare science, and innovative data
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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. Selection
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English IELTS 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