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, 4) Model interpretability. Experience with other deep learning methods, such as Convolutional or Bayesian Neural Networks, Simulation-Based Inference (SBI), Normalizing Flows, or Diffusion Models, is
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, Bayesian statistics, epidemiology, causal inference, statistical learning, artificial intelligence (AI), high-dimensional data, and/or electronic medical records are encouraged to apply. Successful
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Location: Ithaca, New York 14850, United States of America [map ] Subject Areas: Data Science / Statistics , Applied Mathematics , Artificial Intelligence , Bayesian Statistics , Big Data , Scientific
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-informed machine learning. The ideal candidate will have a strong background in developing and integrating probabilistic graphical models, Bayesian networks, causal inference, Markov random fields, hidden
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detection model to a more flexible unequal-variance model in a hierarchical Bayesian approach (Lages, 2024). Techniques used: Computational modelling, Bayesian inference, sampling and simulation techniques
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circuit mechanism underlying higher cognitive functions such as multitasking, rule-based reasoning and Bayesian inference). In addition to the above areas, there is extensive expertise available in
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. We are interested in candidates with research interests in causal inference or Bayesian methodology, and we also welcome strong applicants from the broader fields of statistics and machine learning
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at USC" section on the Applicant Portal at https://uscjobs.sc.edu. Position Description Advertised Job Summary The faculty of the Department of Statistics at the University of South Carolina, Columbia
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high-dimensional neural data. Approaches used include neural network-based approaches, Bayesian inference, and more Assisting with the oversight of day-to-day functions of the lab and shared lab spaces
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to statistical computing, Bayesian modeling, causal inference, clinical trials and analysis of complex large-scale data such as omics data, wearable tech, and electronic health record, with specific preference