43 data-"https:" "https:" "https:" "https:" "https:" "P" positions at University of Twente in Netherlands
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Vacancies Postdoc position on Federated/Continual Learning for Time-Series IoT Data (TRUMAN Project) Key takeaways In this role, you will address the intricate challenge of enabling AI to learn
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? Join our innovative project where simulated data and real hardware testing create innovative solutions. The work builds on our recent results . You will be at the forefront of developing advanced
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19 Dec 2025 Job Information Organisation/Company University of Twente Research Field Computer science » Other Engineering » Computer engineering Researcher Profile First Stage Researcher (R1
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, fundamental research and/or studies involving matters of scientific urgency. Information and application Are you interested in this position? Please send your application via the 'Apply now' button below before
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, 2020 . In this PhD project you will work on applying RNPU networks for solving computational problems that are considered hard. Information and application Are you interested in this position? Please
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. Designing predictive control strategies that regulate muscle-tendon loading via wearable exoskeletons. Implementing and testing control algorithms in simulation and real-time settings. Information and
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, exposing the limitations of current detection timelines. This reactive posture is worsened by a visibility gap in the DNS ecosystem. A lack of transparency in registration data, coupled with the short-lived
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to advance predictive, data-driven production and materials planning incorporating the high variability in high-mix, low-volume (HMLV) manufacturing environments. This position is part of an Industrial
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. Information and application Interested? Send your application before February 15th, 2026 through the provide button and should include: transcript of your MSc course grades CV, including publication list (when
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). The field of Machine Learning on Graphs aims to extract knowledge from graph-structured and network data through powerful machine learning models. Designing provably powerful learning models for graphs will