35 data-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:" "DIFFER" Fellowship positions in Germany
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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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(m/f/d) in Bioinformatics. Project overview This position offers an exciting opportunity to develop machine learning methods that reveal how biological function is encoded across different kinds
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in Japan. The same freedom is given to Japanese Canon Fellows coming to Europe. The financial support for Research Fellows may amount up to 30,000 EUR per year and pro-rata for different periods
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combines academic collaboration, international encounters and focused research. The Fellowship Programme is open to researchers from various disciplines and at different stages of their careers. Applicants
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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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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