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members. Learn more at https://hr.duke.edu/benefits/ Duke is an Equal Opportunity Employer committed to providing employment opportunity without regard to an individual's age, color, disability, gender
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diverse workforce is key to the successful pursuit of excellence in research, innovation, and learning for all faculty, staff and students. Our commitment to employment equity helps achieve inclusion and
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, machine learning, and life cycle assessment, we aim to create sustainable wearable systems to enhance human well-being. For more details, please view https://www.ntu.edu.sg/mse/research . We are looking
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statistical software. Learning Objectives: Learn about the implementation of the application of machine learning methodologies in plant phenotyping and genotyping for the sugarcane molecular biology lab. Learn
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mathematics, and finite element analysis. Programming experience in coding e.g. Python, or MATLAB Writing skills for research papers. Experience in applied machine learning, fault diagnosis, laboratory testing
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knowledge within the realm of political science. They should be proficient in conducting quantitative analyses. Experience with large language models, machine learning, and/or programming in R or equivalent
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University of North Carolina Wilmington | Wilmington, North Carolina | United States | about 14 hours ago
affiliations through creative programs, vibrant student organizations, and a living/learning environment built on core values and uncompromising principles. The Division is a leader and primary spokesperson
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Institute of Systems and Robotics, Faculty of Sciences and Technology of the University of Coimbra | Portugal | 3 months ago
Engineering Research Field Engineering » Electrical engineering Engineering » Electronic engineering Engineering » Mechanical engineering Engineering » Computer engineering Researcher Profile First Stage
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PDF, Dev of new porous transport layer architectures for Proton Exchange Membrane Water Electrolysis
with graduate students, technical officers, and machine learning scientists to design, evaluate and intelligently optimize PTLs with innovative structures, delivering design guidelines for next
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exciting project that will develop new approaches to handle missing data in statistical analyses based on machine learning methods. The Research Fellow will be based in the Department of Medical Statistics