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publication record relative to their career stage and a clear interest in interdisciplinary collaboration. Ideally, you also bring experience with machine-learning or hybrid modelling approaches, as
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crystal handling, SPR machines, and two libraries of fragments (small molecules <300 Da) tailored for SPR and X-ray crystallography. This setup enables a full workflow for fragment-based drug discovery
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the interplay between qualitative and quantitative methods and data. There is a growing focus on novel computational methods such as NLP, machine learning, and AI within the group. Teaching activities in
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with troubleshooting their machines and support their understanding of core concepts. Guide students in working on their own project. Demonstrate best practices and foster the development
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applications from researchers specializing in probabilistic and neuro-symbolic AI. Areas of interest include, but are not limited to: • Probabilistic machine learning • Deep probabilistic graphical models
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medical images and other health data. The group develops and evaluates clinically meaningful decision support tools by integrating health data, domain knowledge, and machine learning. Key objectives include
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transportation engineering, economics, psychology, computer science, social data science, machine learning, mathematics, and statistics. As a candidate, you should have a high potential for creative and
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position A PhD degree or equivalent in history, classical studies or related research areas. Documented experience with research on and teaching digital methods. Experience with didactic integration
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, of which about 90 are PhD students, and about 40 % of all employees are internationals. In total, it has more than 700 students in its BSc and MSc programs, which are based on AAU's problem-based learning
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of teaching experience Academic Diplomas (MSc/PhD) You can learn more about the recruitment process here . Applications received after the deadline will not be considered. All interested candidates irrespective