335 data-"https:"-"https:"-"https:"-"https:"-"CNRS"-"Univ"-"Univ" positions in Netherlands
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Department can also provide assistance during the recruitment process. If you would like to discuss this further, please contact us via email at contact.human.resources@esa.int . Important Information and
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Development (LED) Work, Health and Performance (WHP) In the document, at the very end of the vacancy text (under 'Attachment'), you will find specific information about the four PhD positions. Please read
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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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focus; Coordinating data collection; Teaching Bachelor’s and Master’s degree courses in the curriculum of Interdisciplinary Social Science; mainly thesis supervision. Your qualities We believe in a
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and the EUR Copyright Information Point, you help Erasmus University to take Open Education to a higher level. As an Scholarly Communications Steward with a focus on Open Educational Resources (OER
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fte - 1 fte Introduction ODISSEI develops the Dutch national data infrastructure for the social sciences. It is a rapidly growing consortium of 45 participating organisations working together to improve
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the six universities. For more information, check the individual vacancy pages of the universities, or check the project website www.heritour.eu Opens external About the research project This PhD project
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the recruitment process. If you would like to discuss this further, please contact us via email at contact.human.resources@esa.int . Important Information and Disclaimer In principle, recruitment will be within
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Department can also provide assistance during the recruitment process. If you would like to discuss this further, please contact us via email at contact.human.resources@esa.int . Important Information and
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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