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Computer Vision and Generative AI. The successful candidate will join a dynamic research environment and contribute to cutting-edge research at the intersection of computer vision, generative artificial
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-based vision (event cameras), we are recruiting a research and development engineer within our Computer Science (INFO) and Mathematical and Electrical Engineering (MEE) departments. In collaboration with
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. In particular, he/she will be expected to :• Select and evaluate the most suitable approaches from the wide range of machine learning and computer vision methods available in the literature, with
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 5 days ago
10 Apr 2026 Job Information Organisation/Company Inria, the French national research institute for the digital sciences Research Field Computer science Researcher Profile Recognised Researcher (R2
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Deadline 4 May 2026 - 23:00 (Europe/Paris) Country France Type of Contract Temporary Job Status Full-time Offer Starting Date 1 Jul 2026 Is the job funded through the EU Research Framework Programme? Not
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for the candidate The candidate will not only deepen and enrich their expertise in AI and computer vision, but also become familiar with using AI to handle curvilinear features which are ubiquitous in many domains
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FieldComputer scienceYears of Research Experience1 - 4 Research FieldMathematicsYears of Research Experience1 - 4 Additional Information Eligibility criteria Skills/knowledge: computer vision, neural networks
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Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description This offer is part of the DuraSyS-PAC (PEPRH2) project
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Deadline 11 Mar 2026 - 00:00 (Europe/Paris) Country France Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme
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quite new within the field of computer vision. The neuromorphic design allows for a much higher acquisition frequency but most and foremost much longer acquisition time spans. This makes it ideal