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supported cooperative learning documented by a PhD dissertation and/or research publications. experience in the self-directed management of research projects experience in interdisciplinary research projects
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: marine mammal bioacoustics passive acoustic monitoring tools (PAMguard) bioacoustic machine learning including supervised and/or unsupervised classification, model training, and model assessment database
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. The ideal applicant can experiment with new methods in the field, for example teaching applied machine learning in an accessible manner to students. Such methods can also relate to teaching platform policy
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, peripheral nervous system, immunohistochemistry, satellite glial cells markers and subtypes, and machine learning strategies for quantification from IHC. As a person, you have good interpersonal skills
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, and machine learning methods will be highly desirable. Experience in development and use of EC-Earth (European Community Earth System Model) in relation to aerosols and clouds will be highly desirable
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/mechanical engineering, computer graphics/vision, machine learning, or other related skills that are useful for fabricating new types of physical objects and interfaces excellent programming skills Your
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include quantitative genetics, machine learning applied to agriculture and precision medicine, population genetics, and integrative genomics. QGG is located at the central campus in Aarhus and at Research
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, students, and academic staff to gather feedback and insights on the use and effectiveness of AI-driven educational tools. - Stay abreast of advancements in AI, machine learning, and computational
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analyses using Danish register data and/or large genetic datasets. This may include genetic analyses, causal inference, epidemiological analyses, and clinical prediction modelling using machine learning
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applicant will be expected to teach in English. Qualifications Applicants must have a PhD degree or document equivalent qualifications in a relevant field related to STS, information studies or neighbouring