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. Integrate this neural information into real-time musculoskeletal modeling using our CEINMS-RT framework . Enable neural control of a bilateral cable-driven ankle exoskeleton in post-stroke individuals during
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, Computer Science and Artificial Intelligence. The position is embedded in the Multi-Agent Systems group of the department of Artificial Intelligence of the Bernoulli Institute. Qualifications PhD in Computer Science
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past and/or current research/activities Ability to gather and share relevant information General interest in space and space research Behavioural competencies Education You should have recently completed
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. Qualifications PhD in Computer Science, Economics, Mathematics, or a closely related dis-cipline. Strong background in computational social choice, algorithmic game theory, or AI safety. Excellent research track
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dynamical systems. The position is part of the research project “A Rigorous Framework for Transient Random Dynamics”, funded by the Dutch Research Council (NWO). You will be part of a team with a PhD student
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. For more information, see also our diversity policy webpage: https://www.rug.nl/about-ug/policy-and-strategy/diversity-and-inclusion/ Our selection procedure follows the guidelines of the Recruitment code
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Your job Are you passionate about cancer research? Excited to dive into biological pathways and complex data analyses in an epidemiological setting? Eager to work within an ambitious and
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sustainable way of dealing with their health conditions. The technical challenge within this project lies in the huge variance of the data. Furthermore, the app needs to be able to work with sparse incomplete
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quickly derive the necessary information from noisy, incomplete, real-world data. Furthermore, the algorithms to be developed within this project will be implemented on automated beds that provide fully
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the social origins of these different meanings; 3) improve alignment between (non-)governmental efforts and citizens’ perspectives by demonstrating how these meanings shape responses to information campaigns