111 learning-"https:"-"https:"-"https:"-"https:"-"https:"-"https:" positions in Denmark
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are willing to learn Danish within two years. Danish language training will be provided. Contact information For further information, please contact: Dean Birgit Schiøtt, email: [email protected] , phone: +45 2982
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are motivated to learn EV and small-RNA methods are explicitly encouraged to apply. Required qualifications PhD degree in Plant Science, Plant Physiology, Plant Molecular Biology, Microbiology or a related
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Assistant Professor Position in Transient Electromagnetic Signal Processing, Modelling and Inversion
machine learning, multidimensional inversion, and probabilistic geological modelling to enable efficient mapping in previously inaccessible terrains. The successful candidate will be employed primarily in
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developing new visualisation strategies to aid delineation, as well as developing deep learning methods to enhance photon-counting CT images and better visualise tissue boundaries. The project will also
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are internationals. In total, it has more than 600 students in its BSc and MSc programs, which are based on AAU's problem-based learning model. The department leverages its unique research infrastructure and lab
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(satellites, drones, etc.) through AI and machine learning; 3) validation and feasibility of the introduced technologies through full-scale pilot scale, demonstration, documentation, and simulations
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) the application of remote sensing (satellites, drones, etc.) through AI and machine learning; 3) validation and feasibility of the introduced technologies through full-scale pilot scale, demonstration
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innovation, knowledge sharing, and professional development. Learn more about AAU Energy at www.energy.aau.dk. How to apply Your application must include the following: Application, stating reasons
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education and democratic citizenship Digital learning and education Creative industries and cultural heritage Applications within these areas will be of particular relevance to the faculty, but applications
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, and machine-learned force fields to describe ion transport and interfacial evolution. These models will be extended to mesoscopic and continuum scales (kinetic Monte Carlo, phase-field) to capture