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machine learning. The project combines methodological research in statistics with applications to large-scale social science data. The successful candidates will have the opportunity to collaborate closely
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The project will be carried out primarily at the CLLE laboratory (UMR 5263) at University of Toulouse – Jean Jaurès (UT2J), in collaboration with the Institut de Recherche en Informatique de Toulouse (IRIT
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on AI-assisted reverse engineering of integrated circuits for hardware assurance and intelligence analysis. The project is conducted within the Deep Learning for Perception and Data Science, Safety and
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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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an interdisciplinary program that brings together Warwick Medical School and partners from the School of Engineering and Industry, combining machine learning and computational modelling to solve a
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the proposed methods using operational or experimental water utility case studies. Work collaboratively with academic and industrial partners across the i3WaterS network. Undertake the research required
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in close collaboration with with a postdoctoral researcher and a fellow PhD candidate, you will identify microbial taxa and microbial interactions that are most strongly associated with disease
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of Twente and will work in collaboration with academic and industrial partners, including RWTH Aachen University and Emicap B.V. As a PhD candidate, you will: design, commission and operate a test setup for
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Stein bei N rnberg, Bayern | Germany | 10 days ago
the development of cardiometabolic diseases. The PhD position is based in the Research Unit and will be part of the Integrated Research Training Group (iRTG) of the German Research Foundation-funded Collaborative
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(EO) data can be used to assess different facets of ecosystem functioning in grasslands and forests. Within the project, you will collaborate closely with other PhDs and postdocs to collect field data