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performing Bayesian data analysis on complex data problems. Estimated course enrolment: 45 Estimated TA support: 70 hours Class Schedule: Mondays, 2:00 p.m. to 5:00 p.m. (in person) Sessional dates: January 6
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Responsibilities Quantitative method development and computational modeling (45%) -Develop, implement, evaluate, and advance computational approaches for estimating spatially and temporally resolved air-pollutant
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related field A strong background in machine learning, computer vision, or 3D point cloud processing, as well as in probabilistic modelling and Bayesian inference Very good programming skills, preferably in
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. The practical element of your project will be based on, but not limited to, time series analysis, network analysis, Bayesian inference, Machine Learning, as well as computational simulation of mathematical models
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. The practical element of your project will be based on, but not limited to, time series analysis, network analysis, Bayesian inference, Machine Learning, as well as computational simulation of mathematical models
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Link https://www.ubjobs.buffalo.edu/postings/64290 Employer Research Foundation Position Type RF Professional Job Type Full-Time Appointment Term Salary Grade E.89 Posting Detail Information Position
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The Statistics (STAT) program in the Computer, Electrical, and Mathematical Sciences and Engineering Division (https://cemse.kaust.edu.sa ) at King Abdullah University of Science and Technology
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 3 days ago
Dependent on Qualifications Proposed Start Date 09/01/2027 Estimated Duration of Appointment 24 Months Position Information Be a Tar Heel! A global higher education leader in innovative teaching, research and
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-time, and evidence-accumulation phenomena. Implement simulation, parameter-estimation, and model-comparison methods in Python, MATLAB, R, or related computational environments. Lead and co-author
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qualifications Publications at top machine learning or computer vision conferences (NeurIPS, ICML, ICLR, CVPR, AISTATS etc.) are highly meriting. Expertise in Bayesian methods, generative models, multimodal models