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characterized as an inability to emulate basic human vision skills. Despite significant advances in deep learning-based computer vision systems, many limitations still exist. The main objective of this project is
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science/biomedical engineering or of relevant scientific field A solid background in machine learning Extensive experience with either computer vision or image analysis Good knowledge of deep learning
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, machine learning and computer vision techniques, statistical data analysis, and Python programming. Activities will include field calibration and validation of models and data integration into a decision
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Mathematics (Inverse Problems), Computer Science (Machine Learning, Computer Vision, Efficient Algorithms and High-Performance Computing), and Physics (Image Formation Modelling). Your project is part of
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team players and passionate on cutting edge computer vision and machine learning technologies, as well as possess deep understanding of machine learning technology and experience on turning machine
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 19 days ago
capable of advancing these topics independently. In close collaboration with experienced researchers, the engineer(s) will be in charge of: developing a data acquisition interface developing computer vision
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Science, Computer Engineering, Electronic Engineering (or related disciplines). A strong record of research quality, commensurate with career stage in AI, including but not limited to: machine learning, deep learning
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Science, or a related technical field Master's or PhD degree in Machine Learning, Computer Vision, or related areas will be advantageous Preferred Qualifications: Experience with biological/ecological
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engineering (focusing on deep learning for computer vision), and the division of statistics and machine learning at the department of computer and information science (focusing on the theory behind machine
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behavioural experimental design and statistical modelling; computer vision and AI techniques; explainable AI and human–machine comparison methods; and responsible innovation. The student will work closely with