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. The work combines physics-based thermal design and process-level system simulation with high-fidelity computational fluid dynamics and fast reduced-order and machine-learning models, so that the final design
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environments. Experience with software such as R, Python, SPSS, Stata, Sawtooth, Qualtrics or similar tools will be considered an advantage. The successful candidate should have strong analytical skills, good
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discussions about research, teaching and development of the study programmes, and you approach both research and teaching tasks with an analytical and structured mindset. Who we are The Department of Materials
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analytical skills. Knowledge about statistical machine learning, robotic perception, multimodal AI algorithms. Proven experience with reinforcement learning algorithms and implementations. Experience in
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researcher with a strong quantitative profile, excellent analytical skills, and a clear interest in publishing high-quality empirical research. The successful candidate will primarily work on a randomized
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and other postdoctoral researchers as part of our Lundbeck Professorship grant, which you can learn more about here: https://www.cnap.hst.aau.dk/lundbeck-professorship As a postdoctoral researcher your