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In this thesis you will work in the European Space Agency (ESA)-funded project Fast-EO (Fostering Advances in Foundation Models via Unsupervised and Self-Supervised Learning for Downstream Tasks in
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for Digital Humanities is one of Germany′s leading institutions in the fields of digital and computational humanities. Its key areas of focus are digital linguistics, digital heritage, and AI/machine learning
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various materials Your goal is to identify the factors that limit the models’ generalizability To this end, you will investigate transfer learning approaches to reduce the required training effort and adapt
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to joint research activities, publications, and surveys. Requirements PhD degree (or near completion) in robotics, control, machine learning, or a related field; Strong publication record demonstrating
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profile is shaped in particular by five high-profile areas. About Minds, Media, Machines Minds, Media, Machines (MMM) is one of the five interdisciplinary, high-profile areas that largely define
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The Fraunhofer Institute for Digital Medicine MEVIS is a world-leading and internationally connected research center for computer assistance in medicine. With about 140 employees, our mission is to
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optical communication networks and systems, as well as machine learning, computer vision and compressing digital videos. Become a part of our team and join us on our journey of research and innovation! Be
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related field. Strong background in robotics, estimation, control, or machine learning. Strong proficiency in Python and/or C++ and experience with ROS/ROS2. Demonstrated research experience (e.g., Master’s
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Vacancy for fields of study such as: Automation Engineering, Electrical Engineering, Computer Science, Mathematics, Control Engineering, Software Engineering, Computer Engineering, Surveying
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hash functions for image recognition OSINT and crawling, social media and the dark web / cryptocurrencies Machine learning for data analysis AI security: Attacks on deep learning (classifiers, LLMs) and