jguilleminot_duke's blog

Dear All, 

The 34th Annual Robert J. Melosh Competition for the Best Student Paper in Computational Mechanics will be held at Duke University on October 16, 2023.

Dear Colleagues,

Dear Colleagues,

Abstract submission is still open for the USACM Thematic Conference on Uncertainty Quantification for Machine Learning Integrated Physics Modeling (MLIP) which will be held in Crystal City, Arlington, Virginia, August 18-19, 2022. The deadline for submission has been extended to May 15. More information can be found on the conference website. Please consider submitting and/or registering!

On behalf of the organizing committee, Johann

Dear All,

The 33rd Annual Robert J. Melosh Competition for the Best Student Paper in Computational Mechanics will be held at Duke University on October 21st, 2022.

Dear All,

The 32th Annual Robert J. Melosh Competition for the Best Student Paper in Computational Mechanics will be held at Duke University on October 21-22, 2021. A hybrid format is envisioned at this point, compatibly with Duke University’s policies on gathering under the COVID-19 pandemic.

We are very pleased to announce that Stein K. F. Stoter, from the Department of Civil, Environmental, and Geo-Engineering, University of Minnesota, Minneapolis, USA (adviser: Professor Dominik Schillinger), is the 2020 Melosh Medalist for the Best Student Paper in Finite Element Analysis. Dr. Stoter’s contribution was titled "The variational multiscale method for discontinuous Galerkin type finite element formulations".

Congratulations Stein!

Several positions at the PhD and postdoctoral levels are available in the group of Prof. Guilleminot at Duke University. The ideal candidates should have a particular interest in conducting interdisciplinary research at the interface of computational mechanics, materials science and uncertainty quantification. Topics of interest include (but are not limited to) stochastic modeling and computational frameworks for large-scale nonlinear materials and systems, statistical inverse problems and multiscale approaches.