87 data-"https:"-"https:"-"https:"-"https:"-"https:"-"Stanford-University" PhD positions in Netherlands
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- Delft University of Technology (TU Delft); Published yesterday
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and/or educational projects. Within the Delta Values team, the PhD student will have their own clearly defined tasks, co-developed with the supervisory team. What will you do? collect and analyse data
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and will contribute to theoretical frameworks, common methods, educational toolkits, and knowledge-sharing platforms in co-creation with project partners. For more information, see www.justart.info
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approach that combines semantic material data, design-for-circularity, and hub logistics to scale high-quality reuse in regional infrastructure ecosystems (primary focus: Twente; validation: Brabant). Reuse
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use cookies and similar technologies to process your personal data (e.g. IP address) for personalised content, ads and third-party media integrations. Functional and strictly analytical cookies
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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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employment conditions through our Terms of Employment Options Model. In this way, we encourage you to keep investing in your personal and professional development. For more information, please visit Working
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Investigator (PI), Dr Marzi. Together, you will further develop and refine the PhD case studies, the research approach and the various work packages, as well as analyse data, participate in conferences and
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cookies and similar technologies to process your personal data (e.g. IP address) for personalised content, ads and third-party media integrations. Functional and strictly analytical cookies are always set
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Options Model. In this way, we encourage you to keep investing in your personal and professional development. For more information, please visit Working at Utrecht University . About us A better future for
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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