The Shields Uncertainty Research Group (SURG) at Johns Hopkins University is seeking multiple postdoctoral researchers to join their team in the field of uncertainty quantification for materials modeling in extreme environments. The research focuses on developing novel materials models for scenarios such as blast and impact, where materials are subjected to extreme temperatures, pressures, and large deformations. The postdocs will contribute to the creation of machine learned constitutive models, equations of state, and other material property models, including opacity. This opportunity is ideal for candidates with a PhD in mechanical engineering, civil engineering, materials science, or materials physics. The research environment is highly collaborative, involving multiple principal investigators, postdocs, and students. The work sits at the intersection of scientific machine learning, classical physics and mechanics-based models, and uncertainty quantification, making it suitable for those with expertise or strong interest in these areas. Applicants should have a strong background in scientific machine learning, uncertainty quantification, and/or the study of materials behavior under extreme conditions. The position is based at the Hopkins Extreme Materials Institute (HEMI) at Johns Hopkins University, a leading center for research in materials science and engineering. To apply, candidates should send a CV, a brief (1 page) statement of research experience and interests, and contact information for 2-3 references to Prof. Michael Shields at [email protected], using the email subject 'SURG Postdoc Application.' No explicit deadline is mentioned, so early application is encouraged. For more information about the research group and the position, see the LinkedIn announcement and the university's research pages. This is a fully funded postdoctoral opportunity at a top-tier US research institution.

The Shields Uncertainty Research Group (SURG) at Johns Hopkins University is seeking multiple postdoctoral researchers to join their team in the field of uncertainty quantification for materials modeling in extreme environments. These positions are part of a collaborative research effort involving multiple principal investigators, postdocs, and students, focusing on the development of novel materials models for scenarios such as blast and impact, where materials are subjected to extreme temperatures, pressures, and large deformations. The research will emphasize the integration of scientific machine learning, classical physics and mechanics-based models, and uncertainty quantification. Projects include the creation of new machine-learned constitutive models, equations of state, and models for other material properties such as opacity. The work is highly interdisciplinary, bridging materials science, mechanical engineering, civil engineering, and physics. Applicants should hold a PhD in a relevant engineering field (mechanical, civil), materials science, or materials physics. Desirable qualifications include expertise in scientific machine learning, uncertainty quantification, and/or the study of materials behavior under extreme conditions. The successful candidates will join a dynamic team at the Hopkins Extreme Materials Institute, the Whiting School of Engineering, and the Department of Civil and Systems Engineering at Johns Hopkins University. To apply, candidates should send a CV, a brief (1 page) statement of research experience and interests, and contact information for 2-3 references to Prof. Michael Shields at [email protected], using the subject line 'SURG Postdoc Application.' For more information, see the LinkedIn profile of Prof. Shields or the announcement post. Keywords: uncertainty quantification, materials modeling, extreme environments, machine learning, constitutive models, equations of state, materials science, mechanics, physics, blast and impact, opacity.