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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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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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of quantum mechanics and statistical mechanics. The Computational Biochemistry group consists currently of eight coworkers and combines quantum chemistry, statistical mechanics and machine learning with
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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 radiance data from new hyperspectral infrared instruments such as IASI-NG, MTG-IRS Enhancement of CrIS radiance assimilation algorithm are highly encouraged. - Use machine learning methods to cope with model
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in IEEE Communications Society’s and IEEE Signal Processing Society’s journals and conferences. Strong background in communication theory, signal processing, machine learning, and optimization theory
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Torbein Kvil Gamst 26th April 2026 Languages English English English Faculty of Science and Technology Postdoctoral Research Fellow in Machine Learning Apply for this job See advertisement
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renewed each semester Requirements Minimum of enrolled/completed Master?s degree in a science field or current enrollment in a PhD program. Instructional experience of at least 2 years preferred. Past
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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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flexibility. Responsibilities include conducting behavioral neuroscience experiments—including vapor self-administration and operant conditioning tasks (such as attentional set-shifting and reversal learning