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, or Stata); · Creating and managing very large datasets; · Machine learning skills. Basic Qualifications A Ph.D. in any business discipline, organizational behavior, economics, statistics, environmental
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of subsurface processes. You will be responsible for leading the development of the approach, which could include transferring learning from other geographic regions and data types, machine learning methods
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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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Responsibilities Development of new machine learning modeling approaches Development of new advanced control and optimization algorithms Optimization of carbon capture process operation Provide regular project
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 2 days ago
conference presentations. You will be able to work both independently and as part of a diverse research team. Experience with machine learning, array seismology, or planetary mission data analysis (such as
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development. Familiarity with software development lifecycle including design and documentation of software architecture, testing/debugging skills, and version control. Experience applying machine learning
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factors. Though not required, we are particularly interested in applicants who use advanced quantitative methods, including computational modeling, machine learning, and/or analyzing structural and
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enable students to earn associate or bachelor’s degrees through a combination of in-person, online or blended learning. All of our system institutions place strong emphasis on service — helping to build
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enable students to earn associate or bachelor’s degrees through a combination of in-person, online or blended learning. All of our system institutions place strong emphasis on service — helping to build
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research interests compatible with those of our current faculty in the following research groups: mathematical biology, mathematical and computational finance, numerical analysis, machine learning and data