Job Title: Postdoctoral position in data-driven multiscale modeling

Location of Position: Ann Arbor, Michigan

Employing Institution: University of Michigan

Description of Position: The Computational Physics Group at the University of Michigan has an opening for a postdoctoral researcher in data-driven multiscale modeling. This position can be available as early as January 1, 2021. Research in the Computational Physics Group is focused on developing data-driven, machine learning and some artificial intelligence approaches that interact with a range of computational methods for problems in materials physics, biophysics and in engineering more broadly.

The individual who joins the group for this position will have the opportunity to work on, scale bridging approaches between atomistic and continuum models using a range of data driven methods including machine learning techniques. They also will have the opportunity to collaborate on other projects in system inference, graph reduced-order modelling, and machine learning methods---all in the context of the above computational physics applications.
Experience and a background in continuum methods as well as either Monte Carlo or DFT for materials physics would be ideal for this position. Some experience in data-driven methods would be a big plus.

For our recent research in these areas, please follow this link to the Group’s publications.

The University of Michigan and the Computational Physics Group are committed to a just and inclusive treatment of all, regardless of backgrounds of race, ethnicity, gender orientation, sexual orientation, age and other demographic markers.

Minimum Qualifications: A PhD in engineering, applied mathematics, biophysics or applied physics is required. Expertise in computational science and scientific computing applied to problems in physics (including biophysics) or engineering, as well as an exposure to data-driven modelling are desired. 

Application Procedure: Please respond with a CV and a brief statement of research interests to Krishna Garikipati ([email protected]).

Contact Information: Krishna Garikipati ([email protected])


Closing Date: 1/31/2022