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candidate should hold a Master's degree in either Data Science, Applied Mathematics, Business Administration and Economics or a related field with strong programming skills, particularly in Python. Priority
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focus on examining how AI technology influence identity and mental health in young people within daily and long-term timescales and investigate AI’s applied values in clinical contexts. Stipend 1:User
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the areas of family law and particularly comparative family law. Additional language skills are considered a bonus. The PhD will be supervised by Prof Jens Scherpe, the Director of NorFam. The successful
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learning and change Technology, well-being, learning and inclusion Social sustainability in educational settings Dialogic learning, e.g. discussion-based educational methods Processes of facilitation, e.g
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. The main case will be generative AI for architecture visualization, and possibly computer vision for understanding buildings. Candidates for this stipend should have a background in software engineering or
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technologies. Preferably, the candidate should have an Environmental Management background, although other backgrounds like Environmental science, Sustainability science, Environmental engineering, Biology, and
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, Sustainability science, Environmental engineering, Biology, and the like, will be considered as well. Advanced analytical skills, quantitative skills, and system-thinking skills are key requisites for the position
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Support for accompanying family members through the AAU+1 spouse and partner network Danish language courses Application Procedure To apply, please submit: Motivation letter indicating which of the three
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, Wind Turbines, Structural Design, Structural Materials, Geotechnical Engineering, Wave Energy to Traffic along with many other areas. The research is conducted in a national as well as an international
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requirements. Application for enrollment A cover letter, incl. motivation to apply for the position(max. 2 pages Application form A project proposal incl. a research question, timeline, possible relevant