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Bayesian Index Tracking: optimisation by sampling School of Mathematical and Physical Sciences PhD Research Project Self Funded Dr Kostas Triantafyllopoulos, Dr Dimitrios Roxanas Application
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and learned surrogates with clear statistical validation; Bayesian inverse problems and data assimilation via measure transport and amortized inference; robustness and distribution shift in scientific
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Bayesian ML approaches for path inference; introducing sensors; behaviour classification; resource-constrained active-learning; other IoT applications; microbattery development and field experiments and
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 18 hours ago
Working Title Senior Tenure/Tenure-Track Faculty Appointment Type Tenured/Tenure Track Vacancy ID FAC0005666 Full-time/Part-time Full-Time Permanent Hours per week 40 FTE 1 Position Location North Carolina
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 19 hours ago
of Biostatistics. Specifically, the position works on and provides oversight to several federal and industry research and training grants in the areas of casual inference, Bayesian methods, robust methods, frailty
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). -Interest in Bayesian inference. - Knowledge of non-Gaussian models (heavy-tailed, impulsive) is an asset. Additional Information Work Location(s) Number of offers available1Company/InstituteUniversité
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. The ideal candidate will enhance our biostatistical core and complement or deepen our current department strengths, including, but not limited to: Bayesian methods, big data, causal inference, clinical trials
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. The ideal candidate will enhance our biostatistical core and complement or deepen our current department strengths, including, but not limited to: Bayesian methods, big data, causal inference, clinical trials
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and applied mathematics, statistics, and math education. We are seeking a tenure-track Assistant Professor to join our statistics group and contribute to its growth and success. We invite applications
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version control and containerization (Docker/Singularity) Statistical Modeling: Quantitative data analysis using GLMs, Bayesian methods, or mixed-effect models to interpret complex perturbation datasets