22 assistant-professor-and-data-visualization Postdoctoral positions at Chalmers University of Technology
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at Chalmers. If Swedish is not your native language, Chalmers offers Swedish courses to help you settle in. Contract terms This postdoc position is a full-time temporary employment for two years. Application
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that the application is complete. Incomplete applications and applications sent by email will not be considered. Application deadline: May 9th, 2025 For questions, please contact: Assistant professor, Yinan Yu
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photonics integration. If you're passionate about advancing optical technology, do not hesitate to apply! Information about the division and the department An optical frequency comb is a laser source with
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solutions that pave the way for a sustainable planet and healthy humans. Research topics span from food chemistry and food technology to molecular and data- driven nutrition (Precision Nutrition). In Food
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collaboration with the group led by Asst. Prof. Gina Panopoulou. This includes code development, analysis and interpretation of data, writing manuscripts for publication and presenting work at relevant
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of the Wallenberg Centre for Quantum Technology (WACQT, http://wacqt.se ). The core project of the centre is to build a quantum computer based on superconducting circuits. You will be part of the Quantum Computing
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Join us in developing innovative flow control systems to enhance wind-assisted ship propulsion and support sustainable maritime technology! About us The Department of Mechanics and Maritime Sciences
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simulations. Theoretical X-ray spectroscopy is, moreover, used to aid the interpretation of physical characterization of catalysts. The Competence Centre for Catalysis (KCK) is an interdisciplinary research
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and diversity are substantial foundations in all activities at Chalmers. If Swedish is not your native language, Chalmers offers Swedish courses to help you settle in. Contract terms This postdoc
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digital twin framework, adaptable to: The level of detail available for ship modelling, The quality of risk-related data, and Quantified model and data uncertainties. The project will advance knowledge