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vision models Experience with event-based cameras, neuromorphic vision concepts, spiking neural networks, and/or neuromorphic computing is a plus Experience with, or willingness to learn, ROS 2 for robotic
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, a unique opportunity opens up for you: Explore the potential of machine learning and computer vision to revolutionize autonomous flight systems. In close collaboration with leading industry partners
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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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of intelligent monitoring solutions for laboratory animals. The goal is to use AI and computer vision to promote the 3R principles – in particular, the reduction of animal testing while increasing data density
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-efficient learning, uncertainty estimation, 3D vision. What you bring to the table Good knowledge in machine learning and computer vision Experience in programming with Python and familiarity with PyTorch
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: A completed university degree (Master or equivalent) in computer science, data science, applied mathematics, physics, materials science, or a related field Prior experience in computer vision, deep
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scientific vision, planned research program, and its relevance to AngioCardioScience Two reference letters Please get in touch with any questions For scientific inquiries regarding the positions, please
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optical communication networks and systems, as well as machine learning, computer vision and compressing digital videos. The Applied Machine Learning (AML) group is part of the Department for Artificial
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biological, biotechnological and agricultural systems. The main focus is on machine learning approaches, in particular statistical learning, reinforcement learning, deep learning, and computer vision, as
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computational tools to evaluate models of biological and artificial vision, as part of the DFG-funded Collaborative Research Center (CRC) “Robust Vision” at the University of Tübingen. This is a unique