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interest and documented skills and experience in using computer-based tools to analyse, simulate and predict capture performance of active and passive fishing gears. A track record of publishing in peer
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techniques and data analysis to provide a more integrated picture of life processes in the context of health and disease. To be a postdoc fellow at the AMBER programme you will get unprecedented medical
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techniques and data analysis to provide a more integrated picture of life processes in the context of health and disease. To be a postdoc fellow at the AMBER programme you will get unprecedented medical
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Job Description We are now advertising a new position at DTU Physics for the second half period of our project UltraBat - Capturing Ultrafast Electron and Ion Dynamics in Batteries - generously funded for 4 years by EU Horizon RIA with partners in Denmark, Germany, and France. You will be part...
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. Join us at the Department of Electrical and Computer Engineering, Aarhus University, where we are developing semantic-aware communication that leverage edge AI, semantic reasoning, and efficient time
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scientific areas. We educate both Bachelors and Masters of Science in Engineering and around 825 students are enrolled in our study programs. Furthermore, we also offer an ambitious PhD program. Our PhD
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Programme targeted at career development for postdocs at AU. You can read more about it here: https://talent.au.dk/junior-researcher-development-programme/ If nothing else is noted, applications must be
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backgrounds in: Civil, Architectural, and Environmental Engineering, Computer and Software Engineering, Computer Science, or a related discipline such as Mechanical Engineering. Candidates must have completed a
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an attractive and inspiring working environment with dedicated and highly skilled people. There is a formalized teaching program dedicated to methodological aspects of pharmacoepidemiological research. As a
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contributing to developing and implementing novel algorithms at the intersection of computational physics and machine learning for the data-driven discovery of physical models. You will be working primarily with