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Postdoctoral position in data-driven multiscale modeling

Krishna Garikipati's picture

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.


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. Please

respond with a CV and a brief statement of research interests to Krishna Garikipati



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.

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