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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 16 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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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
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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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learning systems, among others. Successful candidates will be responsible team players and passionate on cutting edge computer vision and machine learning technologies, as well as possess deep understanding
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vision techniques. Additionally, the hired student will assist in corrosion/surface defect detection, object tracking of moving targets, and other machine learning based tasks. The computer vision team
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parameters based on paired visible-light and X-ray images. The developed techniques will be validated on real data. As a candidate, you must have a strong background in machine learning and computer vision, as
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Deep Learning libraries (e.g., Pytorch, Tensorflow, Keras) will be considered a significant advantage; Previous experience in image processing and\or computer vision will be considered an advantage
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Position Details Position Information Job Title Computer Vision Developer Appointment Type Student Employee Job Location Corvallis Position Appointment Percent 100 Appointment Basis 12 Pay Method
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(Kubernetes), serverless computing, and REST API development. Proficient in Python, with basic experience in machine learning or computer vision libraries; familiarity with Vision-Language Models (e.g., CLIP
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at: https://www.umu.se/en/department-of-computing-science/ Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data driven models