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Field
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for candidates with a background in or demonstrated ability to learn about: Bayesian methods, probabilistic machine learning or inverse modelling. Prospective applicants are encouraged to direct informal inquiries
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multimodal foundation architectures. Full-time, TV-L E13, 100%, fixed-term for 3+ years. Position PhD Student (100%) Remuneration TV-L E13 (Fully Funded, fixed-term for 3+ years) Field Representation Learning
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, calorimetry, and synchrotron experimental measurement techniques. Knowledge of AI-based and machine-learning methods is also beneficial. For further information about a specific subject see General syllabus
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Join the Responsible Machine Learning (ML) Group at the Faculty of Computer Science. Led by Prof. Dr. Martin Pawelczyk, who recently joined the University of Vienna from Harvard University, our research
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Job description QuTech, the research center of Delft University of Technology (TU Delft) is hiring for a Doctoral Candidate (PhD) position in partnership with the Questing Doctoral Network
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eligible for an exception to this work arrangement. Alternative work arrangements may also be considered to accommodate candidates as required. To learn more about these options, please contact the NRC
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paradigms that support collaborative processes rather than isolated individual use. Combining perspectives from the learning sciences, Computer-Supported Collaborative Learning (CSCL), Computer-Supported
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First Stage Researcher (R1) Positions PhD Positions Application Deadline 8 Oct 2026 - 23:59 (Europe/Oslo) Country Norway Type of Contract Temporary Job Status Full-time Is the job funded through the EU
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prepared for changes to your work duties after employment. Required selection criteria You must meet the requirements for admission to the Doctoral Programme in Computer Scienc e, see Section 6-1 of the PhD
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or environmental engineering, Mathematics (Operations research) or Computer Science or Machine Learning). Documented knowledge of relevant methodologies, both quantitative and/or qualitative, at master’s level