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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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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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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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at the intersection of AI, deep learning, computational neuroscience, and vision science. You'll develop biologically realistic neural networks to understand how individual differences in the brain shape perception
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computer vision, deep learning, and logical reconstruction techniques. The research investigates how multimodal imaging modalities - including scanning electron microscopy (SEM), photon emission microscopy
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imaging (crucial) Experience with image segmentation, deep learning, or computer vision. Experience with 3D image processing or inverse problems. Experience with experimental research and data acquisition
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experienced with aerial robotics, motion planning, control, manipulation and/or machine learning. Master of Science (MSc) diploma in Robotics, Computer Science, Systems & Control, Aerospace Engineering, Applied