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15 Jan 2026 Job Information Organisation/Company IFP Energies nouvelles (IFPEN) Research Field Engineering » Mechanical engineering Researcher Profile First Stage Researcher (R1) Positions PhD
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Postdoctoral Research Associate who will focus on creating innovative artificial intelligence algorithms for the trusted visualization of large-scale 3D scientific data. This position resides in the Data
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-related field and have demonstrated commitment to teaching excellence and innovation. We are seeking candidates who can teach large lower- and upper-level undergraduate and master’s level courses in AI
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Project Overview We are hiring research engineers interested in advancing the state of the art in data-efficient machine learning at SMART in the new program: Mens, Manus and Machina: How AI
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to the successful completion of a PhD degree. For more information see: http://www.mn.uio.no/english/research/phd/ What are we looking for in you? Personal skills: We are seeking a highly motivated, enthusiastic and
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of Helsinki. The main research fields at the department are artificial intelligence, big data frameworks, bioinformatics, data analysis, data science, discrete and machine learning algorithms, distributed
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training leading to the successful completion of a PhD degree. For more information see: http://www.mn.uio.no/english/research/phd/ All candidates and projects will have to undergo a check versus national
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research group (please find further information at https://www.biologie.uni-konstanz.de/gruber ). You will be developing machine learning-based data science approaches for the analysis of Next Generation
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aspects of data analysis: raw data QC, alignment and pre-processing, primary and secondary data analysis. Learn new approaches and then apply these to undertake a variety of cancer-related analyses
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Inria, the French national research institute for the digital sciences | Pau, Aquitaine | France | 3 months ago
) the exploration of mixed-precision arithmetic in the context of high-order discontinuous discretization methods, and (2) the integration of machine learning techniques to complement and enhance traditional