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our research group, Machine Learning and Computational Intelligence (MaLeCI), which is a dynamic and diverse team of talented and highly-motivated researchers conducting cutting-edge research in Machine
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the Department of Economics and Business Economics, we are looking for lecturers who can teach at Bachelor or Master’s level within, but not limited to, the following fields: Business Intelligence Machine and Deep
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research centre. Research at CFIN include fields such as cognitive neuroscience, neuroimaging, machine learning and biophysics. Investigators at CFIN are supported by state-of-the-art research facilities
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variety of countries, experience with computer assisted coding of big chunks of text and experience with large-N analysis of cross-national data will be positively evaluated. The successful applicant will
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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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, 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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, 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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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