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Computer Science, Data Science, and Artificial Intelligence, in both our West Lafayette and Indianapolis locations, as well as graduate MS and PhD programs. For more information, see https://www.cs.purdue.edu
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/ ) at the Department of Clinical Microbiology, at Umeå University (https:// www.umu.se/en/department-of-clinical-microbiology/ ), the PhD candidate work in the Marie Skłodowska-Curie (MSCA) Doctoral Network GLYCOCALYX
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science » Computer systems Computer science » Modelling tools Computer science » Informatics Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 1 May 2026 - 23:00 (Europe/Paris
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, and how their combination can improve safety signal detection. As a PhD fellow, you will be working with large-scale longitudinal data, managing data, writing scripts, performing statistical analyses
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to their existing curriculum in machine learning, data science, and computational modeling of cognition. Our priority is to attract candidates who are strong in relevant technical areas and who can teach Python-based
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publication record. Outstanding data analytics, mathematical, and computer modelling skills. Excellent interpersonal communication and oral presentation skills in English Self-driven and strong team spirit Open
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, network science, and decentralized machine learning. Welcome to read more about us at: https://liu.se/en/organisation/liu/isy/ks . For more information about working at ISY, please visit: https://liu.se/en
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Brandenburg University of Technology Cottbus-Senftenberg • | Cottbus, Brandenburg | Germany | about 2 hours ago
information on BTU scholarship opportunities on our websites: https://www.b-tu.de/en/international/international-students/help-advice-on-all-aspects-of-studying/scholarships-1 . Academic admission requirements
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» OtherEducation LevelPhD or equivalent Skills/Qualifications Who you are PhD in Computer Science, Machine Learning or related field or 5+ years of relevant industrial experience. Strong track record of developing
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some of the following areas (not all are required): Large-scale data analysis and learning analytics methods Experimental or quasi-experimental design; validity and measurement Working with LLMs