Sort by
Refine Your Search
-
Country
-
Employer
-
Field
-
experiments. The objective is to develop Bayesian causal models and neural networks capable of identifying relevant causal relationships between instrumental parameters and observed anomalies. The work will
-
methods of data analytics (e.g., statistics, stochastic analysis, Bayesian statistical analysis), physically-based hydrology and water quality models, and the use of machine learning tools for modeling flow
-
related to gravitational wave astronomy. The primary aim will be the development of advanced approaches for computational Bayesian Inference to measure the properties of Compact Binary Coalescence signals
-
functional data ”, led by Associate Professor Valeria Vitelli. Successful candidates will work on Bayesian models for unsupervised learning when multiple data sources are available, mostly tailored to the case
-
a spatially explicit predictive model for Everglades vegetation dynamics in response to major drivers. The major objectives are to explore the distribution models that discriminate among prairie and
-
statistical shape analysis, Riemannian geometry, time series and stochastic processes, and Bayesian statistics. Key responsibilities: To carry out research within the framework of the project, under
-
statistical shape analysis, Riemannian geometry, time series and stochastic processes, and Bayesian statistics. Key responsibilities: To carry out research within the framework of the project, under
-
candidate will collaborate with investigators within and outside Duke University. The objectives of the projects are: to identify and validate surrogate endpoints of overall survival using data from cancer
-
with investigators within and outside Duke University. The objectives of the projects are: to identify and validate surrogate endpoints of overall survival using data from cancer clinical trials in
-
mentorship. The goal is to work together with other researchers, students, and staff, to help the applicant and PI mutually achieve their career objectives in a supportive, non-toxic environment. This is a two