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models for high tech industry applications. Your results are expected to be published at leading international venues in machine learning, computer vision, robotics and radar, such as NeurIPS, CVPR, ICRA
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also include generative or predictive modeling of dynamic radar scenes. The project combines methodological machine learning research with experiments on real automotive sensor data. You will have access
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learning and data science professional who wants to make a real-world impact? Do you have a specialisation in machine learning and affinity with project management? Do you want to apply advanced AI solutions
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for data-efficient vision foundation models. Foundation models in computer vision currently rely on massive datasets and brute-force scaling. This leads to high data requirements, hidden biases, limited
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expertise in artificial intelligence, computer vision, human-computer interaction, and psychology. Its technical core lies in developing robust and adaptive visual speech recognition models. Close
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multidisciplinary environments Curiosity-driven and self-motivated working attitude Knowledge of biomechanical modeling, anatomy, vision-based motion capture, machine learning, control systems Keep in mind
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at the intersection of computer vision, micro-electronics analysis, and hardware security, and will work under the supervision of researchers within the Department of Intelligent Systems. The PhD researcher will be
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into the transmission X-ray imaging regime. The developed techniques will be validated on real data. As a candidate, you must have a strong background in machine learning, computational imaging, and/or computer vision
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Machine learning, Image processing, and Computer Vision techniques; Highly motivated to both perform foundational research and apply the developed methods to real-world problems; Highly motivated to work in
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project topics can be found at https://indibrain.eu/recruitment . OPEN POSITION: Project 5: Modeling Individual Brain Differences using Deep Learning & Vision Neuroscience: This project sits