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in engineering applications. This includes working with hybrid approaches that combine advanced damage models with machine learning-enhanced numerical simulations, and exploring how these approaches
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and different industrial outreach activities The candidate has at least the following qualifications - Applicants should hold a PhD in Computer Engineering, Computer Science, or similar - Cyber-physical
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teaching and supervision of bachelor's and master's students in areas related to your expertise. Your competencies We expect you to have: A PhD in electrical engineering, energy engineering, mechanical
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have: A PhD in electrical engineering, energy engineering, mechanical engineering, control engineering, operations research, applied mathematics, or a closely related field. Documented experience in
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of Prof. Georg Madsen, with regular shorter research stays at Aarhus University. The project combines density functional theory (DFT), machine-learned force fields and atomistic simulations to uncover how
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. Your competencies You hold a PhD degree in mechanical engineering or a closely related field, and you have a strong background in solid and computational mechanics. You have solid experience with finite
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the development of new research directions at the department. Your competencies You hold a PhD degree in Electrical Engineering, Control Engineering, Electrochemistry or a closely related field or can document
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the development of new research directions at the department. Your competencies You hold a PhD degree in Thermal Engineering or similar or can document equivalent scientific qualifications at PhD level. Your
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significant contributions to fundamental machine learning research, possessing a combination of mathematical maturity and advanced engineering skills: Education: A PhD in Computer Science, Mathematics
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topics. Your competencies You hold a PhD in a relevant field such as industrial ecology, environmental engineering, sustainability science, environmental management or a related area. You have experience