44 data-"https:" "https:" "https:" "https:" "https:" "https:" positions at Chalmers University of Technology
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Doctoral student in physics-guided foundation model for time-series data Reference number REF 2026-0434 Join us to develop physics-guided, data-driven foundation models for multivariate time-series
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, electrochemistry, modelling and data science? We are seeking a postdoctoral researcher to join the Division of Systems and Control, Department of Electrical Engineering, Chalmers University of Technology
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, pelvis, torso, and breast tissue, where body shape and posture strongly influence seatbelt interaction, occupant motion, and injury risk. The work is carried out through data collection, development of
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project addresses that gap through a mixed-method study combining survey data with in-depth interviews. An interdisciplinary approach, integrating systems and policy perspectives with behavioral
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combines advanced radiation and CFD modeling and the use of spectrometer and camera-based data. You will be part of the groups teams that works on related topics and together we will develop a new
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transitions, utilize empirical as well as estimated data, and explore whether and under what conditions changes can occur as well as the cost-effectiveness and environmental effects of such changes. Research
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visible research, strong industry links and diverse environment create a collaborative setting where ideas grow into real impact. At the division of Data Science and AI , we develop data-driven methods
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systems? This doctoral project explores how complex vehicle systems can detect incidents and provide reliable system data for cybersecurity monitoring and forensic analysis. About us The Department
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microbial communities in full-scale wastewater treatment processes and in laboratory experiments, and use the knowledge gained for refined emission predictions using mechanistic and data-driven
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synthetic populations and mobility digital twins for resilient and equitable transport planning. The project combines agent-based modelling, large-scale mobility data, transport networks, generative