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shared goal: to make the world of tomorrow safer. Be part of change Implementation and application of computer vision approaches, including: Application of methods from security-relevant areas such as
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existing technologies, right through to the tested prototype. The Data-based Methods team at Fraunhofer ENAS develops real-world applications using AI, machine learning and computer vision. The main focus is
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optical communication networks and systems, as well as machine learning, computer vision and compressing digital videos. Become a part of our team and join us on our journey of research and innovation! What
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internationally recognized research program, sustained peer-reviewed funding, and a track record of impactful publications in leading journals. Candidates need to demonstrate a clear vision for expanding a
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optical communication networks and systems, as well as machine learning, computer vision and compressing digital videos. As part of our project with the start-up TwinCloud, we are looking for a Student
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: to make the world of tomorrow safer. Be part of change Implementation and application of computer vision approaches, including: object detection (e.g., using YOLO, RT-DETRv2, etc.) image classification
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in Computer Vision • Experience in nonlinear Optimization The weekly working time is 10-20 hours. The position is initially limited to three months, an extension is intended. We value and promote
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optical communication networks and systems, as well as machine learning, computer vision and compressing digital videos. The Sustainable and Modular Networks (SMN) working group operates in an international
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Max Planck Institute for Intelligent Systems, Tübingen, Tübingen | Bingen am Rhein, Rheinland Pfalz | Germany | about 1 month ago
medicine. Using unique 3D & 4D capture facilities, machine learning, computer vision and advanced graphics, we are modeling human and animals shape and behavior. Laboratory website About the position You
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search will be used to explore combinations. Results: The developed methods are intended to allow the training of balanced but also specialised computer vision models, particularly in the field of face