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and deep understanding of machine learning, artificial intelligence, algorithms, and knowledge of the latest developments in AI. Proficiency in ML tracking/monitoring tools (MLflow, Grafana) and LLM
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thesis is not pre-defined and should be defined by the candidate over the course of the first year. The domain of the PhD thesis must be machine learning and either control theory, path planning, or multi
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Doctoral Researchers (PhD students) to work on deep learning methodologies for machine and robot perception. These positions are funded by the Horizon Europe project OPERA (Open Perception, Learning, and
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conducted in collaboration between Linköping University (LiU) and Lund University (LU). Read more here: https://elliit.se/project/machine-learning-for-sensing-in-distributed-wireless-systems/ Distributed MIMO
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this opportunity? Please email npp@orau.org Qualifications Preferred Qualifications for the Ideal Applicant PhD in Cryosphere Sciences or similar field Strong background in machine learning and neural
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Are you interested in understanding and modeling human capabilities to shape the future of autonomous systems? We are looking for a motivated PhD student to join an exciting research project focused
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functional theory. - Effective Hamiltonian methods for quantum phenomena in solids. - Development of machine learning tools for topological materials. - Experimental studies of magnetotransport in quantum
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of: • machine learning • cybersecurity • distributed systems • privacy-enhancing technologies The research will be carried out within the (team name) at LS2N, focusing on trustworthy AI and cybersecurity
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Hands-on lab experience and/or interest Knowledge on Machine Learning, or other AI techniques Personal skills: Team Worker Initiative in Research and Innovation Flexibility Results-oriented Analytical and
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identification Experience in collaborative and international projects Experience/knowledge in HIL systems Hands-on lab experience and/or interest Knowledge on Machine Learning, or other AI techniques Personal