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models, artificial intelligence, Bayesian models, data visualization, dynamic causal models, dynamic systems models, item response theory, large language models, machine learning, mixture models
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programs and ability to learn new ones. Commitment to inclusive excellence. Pay Range Minimum $27.30, Midpoint $32.50, Maximum $37.70 Salary is based on related experience, expertise, and internal equity
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, and machine learning experience; and (c) some research publication experience. Knowledge of Arabic language is preferred, but not required. Previous work on Arabic NLP is preferred but not required
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on studying the principles of neural computation through recurrent neural networks, dynamical systems theory, and machine learning. - Develop mathematical and computational models of neural networks - Analyze
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the research activities of Pratt Institute under one roof in the Brooklyn Navy Yard (BNY). Since our founding in 1887, Pratt has upheld the belief that education should be accessible to all who wish to learn. As
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 6 days ago
, or other novel/emerging pollutants - Developing / implementing advance machine learning algorithms for environmental datasets - Attention to detail and careful documentation of work products such as How
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the Job related to staff position within a Research Infrastructure? No Offer Description Job Purpose As a University of Applied Learning, SIT works closely with industry in our research pursuits. Our
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FieldMathematicsYears of Research Experience1 - 4 Additional Information Eligibility criteria - Thesis in natural language processing with machine learning, - mastery of NLP and machine learning methods and tools
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) hydrological/hydraulic modelling of urban streams and adaptation measures for current conditions and future climate scenarios. The results from the project will contribute to better estimations of how urban
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–outflow measurements, providing limited insight into what happens inside the systems and leading to substantial uncertainty in design, modelling, and long-term performance predictions. This PhD project aims