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on quantitative phenotyping via generative modelling of quantitative MRI data. This exciting PhD position combines advanced machine learning with medical imaging physics to develop next-generation tools
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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
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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 educational programs
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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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Website: https://engineering.uark.edu/ Summary of Job Duties: The Postdoctoral Fellow will continue investigation of cancer nerve crosstalk in both breast cancer and pancreatic cancer. In both projects
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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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intelligence (including machine learning). Desired Qualifications Publications in health sciences and biomedical journals. Demonstrable interdisciplinary research experience. Deep understanding of machine
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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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, 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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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