12 bayesian-network Postdoctoral positions at Duke University in Ireland-University-Ranking-2024
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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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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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? Collaborate with nationally recognized leaders in radiology, machine learning, and imaging science. Access world-class research resources and interdisciplinary partnerships. Expand your professional network
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. This position is designed for scientists with strong computational and quantitative training who are interested in agent-based modeling, network science, infectious disease dynamics, uncertainty quantification
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, enhancer-promoter communication, disease risk variants, and gene regulatory networks in development, regeneration, and disease. Research models may include patient samples, mouse models, and iPSC-derived
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while collaborating across a multi-institutional research network. Join a nationally recognized research environment committed to innovation, collaboration, diversity, equity, and inclusion. Benefit from
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highly collaborative research team dedicated to advancing our understanding of epilepsy, sleep physiology, and neural networks through cutting-edge clinical and translational research. Working alongside
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and leadership development programming Grant-writing and funding support Teaching and mentoring opportunities Networking and professional advancement resources Individualized mentoring and career
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or physics Generative AI/transformers, agentic AI, deep learning Computational genomics, network modeling, spatiotemporal/functional data analysis, time-series Strong programming in R and Python; best
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architecture, enhancer-promoter communication, disease risk variants, and gene regulatory networks in development, regeneration, and disease. Research models may include patient samples, mouse models, and iPSC