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different hardware backends. Design conventional (GPU-based) deep neural networks for comparison. Publish research articles, regular participation in top international conferences to present your work
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efficient for medicine? If the answer is yes, please continue reading! Join our team! We are looking for a PhD student to work on the topic of shape analysis for medical imaging, tailored for deep learning
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Grant, focusing on the development of novel deep learning tools to recommend reaction conditions for the synthesis of novel TRPA1 inhibitors. The project “A machine learning approach to computer assisted
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for anomaly Detection and diagnostics: Leveraging state-of-the-art machine learning and deep learning models for automated fault detection, classification, and time-till-failure prediction. This will involve
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consumers. You'll gain deep interdisciplinary experience—combining multiple data layers and approaches including bioinformatics, machine learning, food safety management, regulatory science, genomics and user
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group has implemented state-of-the-art deep learning for underwater communications; deep learning models underwater environment based on real data. Our preliminary study shows that state-of-the-art deep
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aims to rethink how soft robots can interact with their environment, focusing on large-area, multi-point contacts—similar to how an elephant wraps its trunk around an object. Unlike traditional robots
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… Requirements Specific Requirements Your profile includes - A Bachelor and Masters degree in law preferably with a strong technology law profile - Proven research skills and a deep understanding of the Law and
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trunk around an object. Unlike traditional robots, which rely on rigid links and localized interaction points, soft robots are composed of deformable materials that can conform to complex geometries and
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-based infrared sources is emerging as a promising and effective tool. In particular, the newly developed high-power intrapulse difference frequency generation (IDFG) based sources extend deep into the mid