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microscopy, optical interferometry, vacuum technology, finite element method simulations will be involved. Applicants should hold a PhD in Physics, Nano-science, Engineering or similar, experience with optics
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algorithms. This work will be evaluated through simulations and with physical drones in mock search-and-rescue missions. This position will involve close collaboration with the PhD student at SDU who is
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the Head of DanSIC-MBH lab, Professor Thomas Levin Andersen, e-mail thomas.levin.andersen@rsyd.dk , phone: +45 22284772. Application deadline February 15, 2026, at 23.59 hrs. (CET). Salary and terms
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requirements using pilot-scale or simulation data Publish high-impact research results and present findings at international conferences Contribute to proposal writing and joint projects with academic and
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. education, work/research experience, language skills and other skills relevant for the position. A certified/signed copy of a) PhD certificate and b) Master of Science certificate. If the PhD is not completed
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Postdoc in pharmacoepidemiology: Long-term safety and benefits of ADHD medication in children and...
refer to medical doctor, PhD, Helene Kildegaard and Professor Anton Pottegård, but he/she will also be expected to contribute to the whole research unit whose research areas span from pharmacoepidemiology
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be developed and implemented in the GEOS-Chem chemical transport model, coupled to the Community Earth System Model. Standardized large wildfire events will be simulated based on historical data and
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the applicant and the Department of Ecoscience. The position concludes after 24 months. Job description The Department of Ecoscience, Aarhus University is looking for a post-doctoral researcher to optimize a
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Postdoc – Performance requirements for biobased construction materials used in the building envelope
assessing mold growth and potential impacts on human health. Advanced skills in moisture transport analysis, including experimental investigation and numerical simulation of building envelopes, are required
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following thematic areas: • AREA 1: Machine learning and AI-driven methods for design, simulation, and optimisation in architectural and construction engineering. • AREA 2: Robotic and additive