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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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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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Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 14 hours ago
optimization or machine learning methods relevant to materials research. 4/2/2026
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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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application! We are looking for a PhD student for sustainable and resource-efficient machine learning. Your work assignments Machine learning has recently advanced through scaling model sizes, training budgets
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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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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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*• Experience in Python or another programming language (projects, GitHub repositories, courses, scientific use).• Training or experience in machine learning and data science applied to environmental or energy
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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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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