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Field
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it; as well as have theoretical skills including algorithm implementation/development and data visualization. Experience and interests include designing machine learning pipelines, building web
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control, machine learning, and differential geometry to work on the development of advanced algorithms to enhance the safety and robustness of human-robot interaction. The successful applicant will engage
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to work on meaningful projects with direct clinical relevance. About the role In this role, you will develop and implement computer vision and deep learning algorithms to analyse CT and MRI data from
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researcher to join the Turing AI World-Leading Fellowship research programme led by Professor Alison Noble. This exciting and ambitious research aims to develop new AI for shared human-AI decision-making in
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includes signal processing with emphasis on development and optimization of algorithms for processing single and multi-dimensional signals that are closely related to applications and applied research
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, working on the development of advanced AI/ML algorithms for battery management systems (BMS) in electric mobility and micro mobility applications. The primary focus will be on creating and optimizing state
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. Are you interested in applying your machine learning and deep-learning expertise to develop cutting-edge ecological and environmental research? The Senckenberg Gesellschaft für Naturforschung invites you to
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project team on “Participatory Algorithmic Justice: A multi-sited ethnography to advance algorithmic justice through participatory design” (PARTIALJUSTICE) to examine issues of justice and participation in
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using jets and heavy-flavor probes. The candidate will also participate in algorithm and software development for the LHCb trigger system as part of the Real Time Analysis Project. A Ph.D. in experimental
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FWO-UGent funded bioinformatics postdocs: Unveiling the significance of gene loss in plant evolution
adaptation, speciation, and biological innovations. This project aims to redefine our understanding of gene loss alongside gene gain in plant evolution, focusing on developing novel genomic approaches