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Exactly: A Bayesian Approach. The project aims to address the challenges in pooling inference, by developing and implementing either exact or asymptotically exact Monte Carlo algorithms in collaboration
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opportunity for a highly motivated and skilled Research Associate/Assistant in statistics to join the EPSRC funded project PINCODE: Pooling INference and COmbining Distributions Exactly: A Bayesian Approach
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University of Massachusetts Medical School | Worcester, Massachusetts | United States | about 2 hours ago
modeling, or machine learning - Experience with large-scale genomic data analysis (e.g., GWAS, QTL, PRS, or multi-omics integration) Strong programming skills in R or Python; familiarity with Bayesian
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), mathematical evolutionary modeling (game theory, dynamical systems, agent-based simulations or other), bespoke probabilistic modeling / (Bayesian) data analysis (e.g., in the Rational Speech Act framework
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and manipulating complex data structures, Bayesian modeling, analyzing nested longitudinal data, and who are familiar with techniques for handling challenging data (e.g., highly non-normal distributions
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biomedical data analysis.Please find more information on LinkedIn:https://www.linkedin.com/feed/update/urn:li:activity:7387170929518379009/
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to submitting your application, please review and update (if necessary) the information in your candidate profile as it will transfer to your application. Job Title: Researcher 4 Department: Medicine | IM
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) The position requires a Ph.D. in a relevant field and evidence of applied research experience. Application Requirements Document requirements Curriculum Vitae - Your most recently updated C.V. Cover Letter
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most recently updated C.V. Cover Letter (Optional) Statement of Research (Optional) Reflective Teaching and Mentoring Statement - See our guidance for writing a reflective teaching statement . (Optional
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to submitting your application, please review and update (if necessary) the information in your candidate profile as it will transfer to your application. Job Title: Post Doctorial Scholar Department: FAES | Food