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expertise/interest in Bayesian methods for addressing measurement error. Ideally PhD within the last 5 years. Advanced level experience with R, desired knowledge of Nimble, Overleaf. Excellent communication
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distribution p(A) is typically incorporated in a Bayesian framework (e.g. enforcing that neighboring pixels are highly correlated). An additional difficulty here is that A is a structured geometric object: an
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in karst using hierarchical Bayesian physical neural networks'' for a fixed period of time (maximum two years) for the duration of the project at the SARLU or Hydrotechnical Engineering. Where to apply
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for estimating soil organic matter dynamics. Demonstrated experience in applying Bayesian statistical approaches to soil science questions. Knowledge in soils and soil management issues of Ohio and the greater
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. Proficiency in Python, MATLAB, or R. Strong quantitative and analytic skills. Preferred Qualifications Experience with evidence-accumulation models (DDM, sequential sampling, Bayesian models). Experience with
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, Categorical Data Analysis, Optimization, Time Series Analysis, Survival Analysis, Actuarial Mathematics, Data Mining and Bayesian Statistics are welcome. Candidates are also expected to teach post graduate
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, Bayesian statistics) Good collaborative abilities, independence, and critical thinking. Preferred qualifications In-depth experience with LLM agents Experience with uncertainty quantification in LLMs
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quantitative and analytic skills. Preferred Qualifications Experience with evidence-accumulation models (DDM, sequential sampling, Bayesian models). Experience with computer vision tools (e.g., MediaPipe
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equations, Bayesian inference, large-scale computational methods, bioinformatics, data science, machine learning, optimisation, numerical methods. Please read more about the position and our department on our
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and Statistics we conduct research within the theory and implementation of biomathematics, biostatistics, spatial modeling, differential equations, Bayesian inference, large-scale computational methods