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Field
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machine learning and with strong programming skills, and with an interest in working in close collaboration with industry. Working environment: The project will be done in an interdisciplinary team
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studentship affiliation The successful candidates will work at the machine learning group at UiT and will formally be affiliated with the Department of Mathematics and Statistics and collaborate closely with
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machine learning and with strong programming skills, and with an interest in working in close collaboration with industry. Working environment: The project will be done in an interdisciplinary team
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for feature extraction, which can serve as parameters in simulation. Alternatively, machine learning methods can be employed for comparison with the primary analysis-based approach. The objectives of this PhD
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for feature extraction, which can serve as parameters in simulation. Alternatively, machine learning methods can be employed for comparison with the primary analysis-based approach. The objectives of this PhD
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of various sensors in care for older adults and how comprehensive data can be analyzed safely with knowledge-based machine learning. Moreover, the candidate will shed light on the questions of how data can
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work with the ethical and financial issues surrounding the use of various sensors in care for older adults and how comprehensive data can be analyzed safely with knowledge-based machine learning
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cand.med.vet. degree, with a learning outcome corresponding to the descriptions in the Norwegian Qualification Framework, second cycle. The applicant must have a documented strong academic background from
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five-year master’s degree or a cand.med.vet. degree, with a learning outcome corresponding to the descriptions in the Norwegian Qualification Framework, second cycle. The applicant must have a documented
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emerging field that applies computational techniques such as big data, machine learning, artificial intelligence, optimization, etc. for realizing sustainable future energy systems (smart grids, smart homes