The Nguyen Lab and Elbert Lab invite applications for a postdoctoral position in data-intensive modeling and analysis of architected polymer blends, starting as soon as possible. The position is part of a funded NSF-DMREF grant to create Materials Architected by Adaptive Processing in accord with Materials Genome Initiative (MGI) goals.
There are multiple openings for postdoc positions on the intersection of Computational Mechanics and Scientific Machine Learning in the Bouklas Lab (https://blogs.cornell.edu/bouklaslab/) in Cornell MAE.
The work will focus on automated constitutive modeling, solution of PDEs and software development.
Researcher Positions at Microstructure Solid Mechanics Group, State Key Laboratory of Nonlinear Mechanics, Institute of Mechanics, Chinese Academy of Sciences
I. Introduction to the Team
Dear colleagues,
We, the AMD IiSS Technical Committee organizers, are pleased to continue our long-standing tradition of arranging a symposium on "Instabilities in Solids and Structures" at the annual ASME-International Mechanical Engineering Congress and Exposition (IMECE). The Congress this year will be held in New Orleans, LA during the period of October 29-November 2, 2023.
The "Presentation Only" abstract submission deadline this year is June 23, 2023.
We're excited to share the news that we have a Postdoc opening in our fleXLab (Flexible Structures Laboratory) at EPFL in Switzerland. This position is focused on the mechanics of slender structures. We appreciate the unique perspectives that individual research interests bring, so we'll work to align the specifics of the research project with the successful candidate's interests.
An article published in the latest edition of Futurities - Simulation-Based Engineering & Sciences Magazine, discusses in detail about using Virtual Material Testing and AI to predict the material properties of complex microstructures that are difficult to measure in practice.
Introduction
The purpose of this blog post is to briefly outline a perspective on the emerging opportunities and challenges that arise as the field of Machine Learning (ML) is quickly driving the development of tools that are becoming mainstream in the study of solid mechanics and computational mechanics, with a focus on constitutive modeling. By no means is this a complete review of ML-enabled constitutive modeling.
Background on Scientific Machine Learning (SciML)
The microMechanics of Deformation Research Group (mMOD) in the Department of Materials Science and Engineering at Rutgers University is seeking a Post-Doctoral Associate to perform research as part of an NSF CAREER award project aimed at understanding the fundamentals of fracture in metals. Research tasks will include the development of a machine learning framework for training surrogate models which couples with the finite element method and analyzing large atomistic simulation datasets.
