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Job Description If you want to contribute to pioneering quantum-enhanced biomedical imaging in a leading international research environment, we might have the position for you. At DTU Physics
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Job Description Many industrial processes generate wastewater with small oil droplets stabilized by surfactants, the same goes for accidental spills of fuels incl biofuels and various process oils
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Job Description We are developing a fast, cheap, environmentally friendly way to mineralise CO2 , to make stable, useful products. We invite enthusiastic applicants to join our cross disciplinary team. We seek a Postdoctoral Fellow with expertise in geochemistry or mineralogy or materials...
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competencies The applicant must hold a PhD in a relevant field, such as physics, chemistry or materials science, with expertise in thin film growth and characterization. The ideal candidate should have a strong
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understanding of the physical processes of the wave structure interaction. The postdoc position aims at developing a state-of-the-art methodology for accurate testing of such structures in our physical wave basin
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. Possible tasks include modelling and/or operating bioreactors for conversion of H2 and CO2 to e-methane. A background within microbial catalysis, gas-liquid mass transfer principles, process modelling
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opportunity to join the ERC-funded project “ALPS - AI-based Learning for Physical Simulation”. Expected start date and duration of employment These are 1–year positions from 1 May 2026 or as soon possible. Job
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will: Develop and implement model-based and data-driven (AI) optimization algorithms for battery charging Integrate physics-informed models and data-driven tools to design health-aware charging protocols
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particular emphasis on automated decision-making in autonomous cyber-physical systems. The application areas cover a wide spectrum of autonomous systems, including AGVs/UGVs, UAVs, USVs, and related systems
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physical location of this position will be at the Copenhagen campus of Aalborg University. Job Description This position is part of the cross-disciplinary DK-Future project – Probabilistic Geospatial Machine