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Field
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development with Python Experience of machine learning with PyTorch Good knowledge of machine learning and computer vision algorithms Ability to work on own initiative and in a team Experience in collaborative
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multi-agent autonomous systems and related technologies. This will include development of distributed monitoring algorithms enabling agents in a multi-agent swarm to autonomously locate other agents in
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include supervision of projects, teaching courses at bachelor's and master’s level, and examination as well as possible supervision of PhD students. Furthermore, you should contribute to the development
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at the University of Sheffield within the consortium is to lead nationally the development of quantum machine learning (QML) algorithms. The research will involve designing innovative QML approaches and collaborating
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Wattenhofer and Dr. Antonio Di Maio . You will be entrusted with designing, developing, and evaluating data-driven methods, algorithms, and systems for three independent but related research directions in
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contributing to developing and implementing novel algorithms at the intersection of computational physics and machine learning for the data-driven discovery of physical models. You will be working primarily with
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for three consecutive periods (2014-2018 and 2018-2022 and 2023-2026). ICN2 comprises 19 Research Groups, 7 Technical Development and Support Units and Facilities, and 2 Research Platforms, covering different
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objective is to develop a next generation of AI approaches that are more sustainable and accessible. Relevant domains include mathematical and computational optimization, learning algorithms, statistical
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design, development, and validation of material, control systems, and algorithms for next-generation soft haptic actuators and experiences. Note that the research involves significant interactions with
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of stroke patients and healthy volunteers. Developing algorithms for identifying and excluding motor unit filters associated to impaired motor units. Integrating real-time-decoded features of motor unit