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16 Mar 2026 Job Information Organisation/Company UNIVERSIDAD POLITECNICA DE MADRID Department HRS4R Research Field Engineering » Materials engineering Researcher Profile Recognised Researcher (R2
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Simulations of Microstructure driven Mechanical properties from high Throughput and multiscale analysIS) project which is based on an effective collaboration between LEM3, ICA, PIMM and CEA. The PhD candidate
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, computer science, artificial intelligence, or mathematics/statistics, with a PhD in one of these fields or a closely related field, and interested in applying this skillset to the analysis of music data. The place
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thermodynamic analysis of chemical reactions. The PhD will be supervised by Sébastien Leveneur (HDR) and carried out within the framework of the ANR QUANTASTIC project, in close interaction with partners in
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opportunity to design and carry out an innovative PhD project titled ‘Meritocracy as Political Culture: A Comparative Analysis of Structures, Attitudes, and Meanings,’ which examines meritocracy as a dynamic
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individual meetings. • Present data and findings to research groups in the University and at national meetings. Qualifications Minimum Qualifications: PhD in a relevant field (Biochemistry, Molecular
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Energy and Environmental Economics, focusing on macroeconomic analysis of energy transition policies
12th April 2026 Languages English English English We have a vacancy for a PhD Candidate within Energy and Environmental Economics, focusing on macroeconomic analysis of energy transition policies
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and further information go to: https://www.uni-muenster.de/CiM-IMPRS We offer 16 fully financed PhD positions. More positions financed by work contracts may be offered depending on availability
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outcomes of therapy. The lab is looking for candidates for the following two stipends: • Stipend 1: Computer Vision-Based Analysis of Humans. This PhD candidate will focus on developing new AI/computer
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for the identification of floating material (plastic and wood) 2. Creation of ground truth for satellite data 3. Development of geomorphological process indicators from satellite classifications 4. Analysis of data