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on the resulting algorithms and pipelines. As an emerging paradigm, differentiable programming builds upon several areas of computer science and applied mathematics, including automatic differentiation, graphical
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structure, and of the force and tidal field that has been shaping the cosmic web. The basic detection algorithms to infer the overall structure of the cosmic web are the various versions of the scale-space
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of Earth observation, navigation, science, and connectivity and secure communications. The AOCS and Pointing Systems Section carries out technological research and development, harmonisation and
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Recognition Machine Learning and Pattern Recognition are subareas of AI aimed at the development of algorithms and models capable of learning from data, recognizing patterns, and signal analysis. Tasks include
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embedded within the department Law & Markets, home of a vibrant and diverse academic community. The overarching mission of the department Law & Markets is to develop and deliver cutting-edge, high-quality
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for recognition of planetary materials from multispectral datasets. Interns are sought to contribute to the ongoing development of Machine Learning algorithms for recognition of planetary materials from
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Division, Systems Department, Directorate of Technology, Engineering and Quality. The Future Engineering Division, in support of the programme directorates, is responsible for developing and providing
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of computer science and applied mathematics. Areas of research are partly chosen by you based on expert judgment, insights into trends and developments, and partly chosen by the team to follow the strategic directions