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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
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about state-of-the-art methods in machine learning, reinforcement learning and computer vision for the life sciences Your Profile: Excellent Master’s degree in engineering, computer
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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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institution. At the Faculty of Computer Science, Institute of Artificial Intelligence, the Chair of Machine Learning for Computer Vision offers two full-time positions as Research Associate / PhD Student (m/f/x
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computer vision methods and their applications Your Profile: Excellent Master’s degree in engineering, computer science or mathematics (or a related field), with a focus on computer vision, image processing
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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! Be
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infrastructure to research solutions and to transfer our knowledge to society. We are doing this in line with our vision: “Taking the pulse of our Earth to safeguard a habitable planet”. For section 4.4 Hydrology
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skills, preferably some experience with PyTorch. Ideally, knowledge in computer vision and object detection/segmentation. Motivation to independently delve into new and current research topics. Willingness
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advanced technologies and infrastructure to research solutions and to transfer our knowledge to society. We are doing this in line with our vision: “Taking the pulse of our Earth to safeguard a habitable
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Fraunhofer IGD and the FBN team to safeguard an efficient collaboration and communication between behavioural biologists and computer scientists. The project is part of the KI-Tierwohl project (https://ki