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materials and technologies. Using advanced computational modeling and machine learning, we seek to elucidate the mechanisms governing the self-assembly of lignin in different solvents and the formation
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benchmarking of deep learning sequence-to-sequence architectures Implementation of new machine-learning layers and model components Application of tools for genome analysis and molecular evolution The position
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at the interface of machine learning, statistics, and live-cell biology. The position is co-supervised by Prof. Olivier Pertz (Cell Biology) and Prof. David Ginsbourger (Statistics), and the student will be equally
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transitions in and out of campus housing, accurate data reporting, and collaborative partnerships across departments. As part of our integrated residential education model, you’ll work closely with professional
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to work in a variety of learning environments, including classrooms, libraries, computer labs, and event spaces.Ability to communicate clearly in person, over the phone, and via electronic platforms.Must be
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metropolitan research university that provides an accessible, quality education through flexible learning and unparalleled internship opportunities. At UA Little Rock, we prepare our more than 8,900 students
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biology and tissue regeneration, modeling painful diseases using human-focused experimental models, and developing novel approaches to treat chronic pain are encouraged to apply. Qualifications Qualified
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and knowledge needed for most core programs at AU. Tutors model effective study habits and learning strategies, equipping student athletes with the skills and knowledge to work and succeed independently
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language models from LLMs. Demonstrated publication record in the machine learning and AI field. Excellent programming and computer science skills. Preferred Qualification: Doctoral degree in electrical
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University of California, San Francisco | San Francisco, California | United States | about 10 hours ago
agreement. The salary range for this position is $27.83 - $59.15 ( Hourly Rate). To learn more about the benefits of working at UCSF, including total compensation, please visit: https