24 data-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:" "https:" Postdoctoral positions 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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research project PRONTO at the intersection of applied mathematics, automatic control, and data science, at the Department of Mathematical Sciences. About us The Department of Mathematical Science
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machine learning methods, statistical analysis and efficient algorithm and data structures ( https://CGRlab.github.io/research/ ). About the research project We are open to discussing research
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between teachers and researchers based on the needs and interests of the schools. For more information ULF at Chalmers see: https://www.chalmers.se/institutioner/mv/resurser-och-samverkan/ulf-praktiknara
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and Policy group at Chalmers as a postdoctoral researcher and help develop the data-driven foresight methods to find out. About us At the Division of Physical Resource Theory, we study how societies
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computing disciplines. Our internationally visible research, strong industry links and diverse environment create a collaborative setting where ideas grow into real impact. At the division of Data
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consequences of lacking maintenance are increasing. This means that precision in asset monitoring and analysis of monitoring data, as well as identification, prioritisation and execution of maintenance
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times as much. No existing method can separate these at scale. This project addresses that gap by combining household transaction data with a new generation of models based on firm-level business
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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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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