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computational approaches to digital education data. The position is funded through a collaborative project between the Hector Research Institute of Education Sciences and Psychology and the Max Planck Institute
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at least 3177 € (net salary after taxes and health insurance payments). Your income will increase regularly if you remain employed. Information regarding cost of living in Tübingen: https
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to Article 13 of the General Data Protection Regulation (GDPR) regarding the collection and processing of personal data in connection with your application, available at https://portal.mytum.de/kompass
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allowance for fellowship holders travelling from Spain. Application Papers A link to the application forms and further information can be found here . Application Deadline Application deadline is 10 January
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extensive alumni sponsorship Application Papers Links to the application forms and further information on the application procedure can be found here . Application Deadline Applications can be submitted
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. Application Papers You can find information on the application process and the link to the online application portal here . Application Deadline The programme announcement will usually be published in August
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covered by other funding providers: Further information Further information For further information see: www.daad.de/dlr Application requirements What requirements must be met? At the time of
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Max Planck Institute for the History of Science, Berlin | Berlin, Berlin | Germany | about 1 hour ago
accepted. Applications must be uploaded to the application portal on or before 15 January 2027 (23:59 CET). Finalists may expect a decision by 1st April, 2027. For further information, please consult
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individually, further information . For fellows applying to a funding period exceeding six months, family benefits may be granted subject to eligibility. Eligibility and the level of support will be determined
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(m/f/d) in Graph Learning. Project overview This position offers a rare opportunity to contribute to the future of machine learning for graph-structured data. While machine learning has transformed