PhD Opportunity: Bayesian Uncertainty Quantification for Multimodal Data Fusion

I am looking to recruit a PhD student for a fully supported position to work on a project focused on Bayesian uncertainty quantification and data fusion for heterogeneous, multi-modal data. The project aims to develop models and algorithms to characterize or approximate different marginal posterior distributions when information is drawn from multiple experiments, instruments, or modalities with partial overlap (with marginals constructed and sampled “on-demand”, and with an emphasis on doing so efficiently as new questions or data arise).

The research will involve a mix of mathematical and numerical methods together with practical implementation, and will be demonstrated on heterogeneous datasets from materials science and chemistry.
kreyes3@buffalo.edu