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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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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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, 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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tools; and a solid background in system‑design methodologies. A keen interest in complex dynamical systems, functional and performance engineering, multi-domain methods, learning techniques, and
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supervision signals (e.g., labels in a downstream task or symbolic constraints). You will perform machine learning research, developing a framework for learning interpretable and robust concepts with
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Website https://www.academictransfer.com/en/jobs/358703/phd-in-scalable-safe-ai-for-sem… Requirements Specific Requirements A master’s degree AI, Machine Learning, Data Science, Computer Science or a
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28 Feb 2026 Job Information Organisation/Company Delft University of Technology (TU Delft) Research Field Engineering » Computer engineering Engineering » Simulation engineering Researcher Profile
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to the adaptation of the Environmental Noise Directive for these new technologies. Your main focus will be to develop machine learning-based drone noise models that will be able to generate an accoustic footprint
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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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is made for you! Information We invite highly motivated students with a strong background in mathematical control theory, and a keen interest in machine learning to apply for the PhD position within