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of applying molecular models at process scales, the project combines efficient mathematical concepts like automatic differentiation with backpropagation – the same concept that powers machine learning and
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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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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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, or machine learning models). Experience with high-performance computing and version control (e.g., GitHub). History of large-scale project implementation work in an international setting (e.g, population
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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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mathematics, Earth science, or a related discipline Skills in numerical modelling, programming, and handling large datasets Prior experience in machine learning is desirable Interest in nonlinear dynamics and
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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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Grade Level 44 Salary Range $21.06-33.69/hour Type of Position Staff Position Time Status Full-Time Required Education AS Click here for more information about equivalencies: https://hr.uky.edu/employment
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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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groups and individuals and through mass media. Ability to use the computer for program delivery and management. Ability to visit clientele at sites throughout the county. Ability to plan and teach