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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
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real estate value increments and conduct a study into how LVC can cover the expected costs of climate infrastructure and public goods. You will feed this data into a predictive AI model (developed by
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support systems, specifically simulation games and geospatial digital twins. These systems use detailed representations of a real-world environment and its processes by linking to a myriad of data and
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Organisation for Applied Scientific Research (TNO) and four universities of applied sciences. More information and links to all 10 PhD positions: www.justart.info The complexity and scale of the climate crisis
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. For more information, please visit Working at Utrecht University . About us A better future for everyone. This ambition motivates our scientists in executing their leading research and inspiring teaching. At
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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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services (approximately six months). Candidates are also expected to spend a small number of hours on teaching duties. For more information about the PhD trajectory in general, please contact Suzanne van de
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experimental campaign to characterize its performance. You will be responsible for setting up a state-of-the-art data acquisition system to monitor temperatures, flow rates, and power consumption. Your
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handle your personal data and internal and external candidates. Apply now Application deadline 31 March 2026 We would like to recruit our new colleague ourselves. Acquisition in response to this vacancy
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sensory neurons. You will have the opportunity to integrate different techniques and analyze data sets to understand which unique properties of motor and sensory neurons make them more vulnerable to