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Bayesian inference, likelihood-free inference, uncertainty quantification, identifiability analysis, or scientific machine learning. Strong programming skills (Python, Julia, Matlab, C++, or similar). Strong
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Bayesian meta-analyses. This position also provides opportunities to develop innovative statistical methods related to clinical trial design, variable selection in high-dimensional data, prediction
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health, health disparities, causal inference, and real-world evidence. You will play a major role in the NIH-funded AMEND (Ascertaining Multilevel Drivers of Head and Neck Cancer Disparities) R01 study
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with causal inference methods or machine learning approaches Demonstrated experience in scientific writing and publication Ideal for candidates who: Have recently completed (or are near completion of) a
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Position Description The Signal Inference, Information and Learning (SIIL) Group is seeking a Postdoctoral Researcher to perform research in the area of statistical signal and array processing with
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examining the impact of AI interaction on social cognition, emotional regulation, group collaboration, and collaborative learning using eye-tracking, physiology, EEG, and novel computational measures
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sciences. Essential skills and experience: • Strong background in experimental design and/or causal inference (lab, field, or online experiments) • Extensive experience with data analysis (e.g., R, Python
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that will prepare the Research Associate for the future career in academia or industry. Dr. Mazurowski’s laboratory has a strong track record of publications by the trainees and the trainees of the Mazurowski
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. We are seeking applications from researchers with a demonstrated track record in developing independent research and who are motivated to work at the nexus of multiple disciplines. The postdoctoral
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for the highly motivated individual with a track record of productivity, attention to experimental rigor and reproducibility, and the ability to work both independently and collaboratively are essential. Duke is