18 data-"https:" "https:" "https:" "https:" "https:" research jobs at Chalmers University of Technology
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Postdocs in Data-Driven Product Realization Reference number REF 2026-0393 Join us for an exciting postdoctoral position to shape the future of data-driven product realisation in close collaboration
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Applied Researcher in Data-driven Nutrition Reference number REF 2026-0011 We are seeking an ambitious applied researcher who wants to work at the forefront of data-driven nutrition and health. This
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Data och Analytics Engineer Reference number REF 2026-0400 Vill du vara med och bygga upp en ny, modern plattform för data och analys för ett av Sveriges ledande tekniska universitet? Chalmers bygger
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-making for sustainable cities? Guided by building science researchers, this project assistant role sits at the intersection of data science, building performance simulation and human-computer-interaction
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various ongoing research projects. Duties will include developing and testing software for robot (e.g. Softbanks Robotics "Pepper", and other similar platforms), setting up local databases for data
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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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characterization. • Data Analysis: Automate measurement routines (Python/MATLAB) and analyze complex datasets to validate the trimming process. • Collaboration: Work closely with fabrication teams and
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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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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