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discipline. Experience with deep learning framework PyTorch or similar. Strong background in machine learning, image or signal processing. Knowledge of SotA models for multi-modality and scene understanding
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-scale compound drivers. We will leverage machine learning methods to bridge the gap between drivers at coarse model resolutions and impacts captured by high-resolution observations. Job description Arctic
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Mathematics (Inverse Problems), Computer Science (Machine Learning, Computer Vision, Efficient Algorithms and High-Performance Computing), and Physics (Image Formation Modelling). Your project is part of
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, silicon-proven AI/ML accelerator for transmitter error correction (digital predistortion/calibration). Your work will sit at the intersection of machine learning, DSP, and digital IC design, and you will
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You will join the “Professional Learning & Technology” (PLT) section of the Faculty of Behavioural, Management & Social Sciences. The PLT section specializes in research on professional learning in and
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are promoting more resource intensive lifestyles under banners of health, safety and pleasance. While the Human-Computer Interaction (HCI) community shaping smart technologies is still coming to terms with its
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boundaries of system-level modelling, analysis, design, exploration and synthesis beyond the current state-of-the-art? Or are you curious to learn more about the application of AI for system design? We
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of moving to a non-English-speaking country posed a challenge. I questioned my supervisor multiple times about the necessity of learning Dutch. Fortunately, she assured me that the English level is very high
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. Methodological Approach Candidates will develop and apply state-of-the-art machine learning techniques, including deep learning, representation learning, variational autoencoders, and graph-based models. A strong
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applicants should have a strong academic record with a solid background in Machine Learning. Knowledge of Vision-Language-Action models and Novel View Synthesis techniques is a strong plus. Good programming