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. Qualifications and personal qualities: The applicant must hold a Norwegian PhD or an equivalent degree within information or computer science, computational social science or related fields. It is a condition that
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, Computer Science, Applied Mathematics or another relevant subject. Good Python programming skills. Knowledge of data science. Experience with artificial intelligence and machine learning. A good command of
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the regulations of Ministerial Order No. 1124 of September 19, 2025 on the PhD Programme at the Universities and Certain Higher Artistic Educational Institutions. According to the Ministerial Order, the progress
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of the Data and Artificial Intelligence unit at the Department of Computer Science. About the project The position is funded by the Norwegian Research Center for AI Innovation - NorwAI - and will be based
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EU MSCA doctoral (PhD) position in Materials Engineering with focus on mechanistic study of high cor
PhD Position – Ad Print4Life DC14 Reference code: 50156137_2 – 2025/MO 5 Commencement date: March 1st, 2026 Work location: Geesthacht (near Hamburg) Application deadline: December 31st, 2025 EU MSCA
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speech, test infants’ reliance on sound cues, and use computational models to simulate the learning of two languages from speech. Your job In the PhD subproject, you work with Dutch-English bilingual
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Description Where: At the Chair for Computational and Theoretical Biology ( CCTB ) and the Center for Artificial Intelligence and Data Science ( CAIDAS ) of the University of Würzburg. Project
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12th December 2025 Languages English English English The Department of Computer Science has a vacancy for a PhD Candidate in Human-AI Interaction and Learning Apply for this job See advertisement
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technology. As a PhD student, you join this prestigious Future Network Services (FNS) flagship project, where research and entrepreneurial pursuits converge. You will join the Signal Process Systems (SPS
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, many publications lack complete, structured representations of their computational processes. This PhD project involves a GenAI-based approach to replicate and rebuild scientific data analysis workflows