Profs. Michael Shields and Lori Graham-Brady are seeking a postdoctoral scholar for a joint collaborative program on uncertainty quantification of mechanical property predictions based on data-driven and/or machine learning models. In the short term, the focus of this project will be on developing UQ tools for Deep Neural Network models that connect image data to material properties. In the longer term this is expected to expand to a spectrum of ML and other data-driven models. Finally, we hope to explore UQ for interconnected models, e.g., in a hierarchical multi-scale construct.
Successful candidates for the position will have a demonstrated track record of scholarly research, and experience in uncertainty quantification, machine learning, and/or computational mechanics. Due to the broad range of expertise requested, we are open to candidates who have more significant expertise in one of these areas and who are interested in developing further knowledge in the other domains. Initial appointment is for one year with the expectation of renewal for a second year pending satisfactory performance. We are committed to recruiting a diverse community of faculty, students, and staff, and to cultivating an inclusive environment that supports, fosters and celebrates all the ways in which the broad differences among us make us better.