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be eligible for an exception to this work arrangement. Alternative work arrangements may also be considered to accommodate candidates as required. To learn more about these options, please contact
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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 fairness
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the use of workflow tools, development environments, and resources to contribute to and implement shared bioinformatic workflows. Experiences may extend into training on Machine Learning and AI models as
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learning, growth and productivity while affording a basic need to its students – a chance to advance. The 15:1 student to teacher ratio makes it possible to maintain a learning environment with close
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learning, growth and productivity while affording a basic need to its students – a chance to advance. The 15:1 student to teacher ratio makes it possible to maintain a learning environment with close
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, mechanical, industrial, biomedical, chemical, manufacturing, aerospace, or computer. The candidate must: Be a United States citizen of Italian descent Be a student currently attending, full-time, an accredited
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, simulations, AI, and machine learning applied to proteins. A track record of research outputs, including publications and presentations at national or international level. Excellent communication and
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such as Wildfires. Platforms include Satellite, Aircraft, Drones, Mobile Devices using Optical, LiDAR, Navigation sensors. Highly automated Machine Learning and Geo-AI workflows underpin an intuitive Common
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Management Science, Information Systems Research, and MIS Quarterly. We seek applicants with a strong quantitative background, such as analytical modeling, econometrics, and/or machine learning, and a
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experiments, behavioral research, econometric and causal inference approaches, optimization and analytical modeling, and data-driven techniques such as machine learning and large language models. Our work is