📢 Call for Abstracts 📢
Mini symposium Applications of Advanced Numerical Analyses in Oil and Gas Industry, WCCM-ECCOMAS 2026
Organizers: Allan Zhong (Halliburton), Amandine Battentier (SLB), Haitao Zhang (SLB)
I’m excited to announce that our mini symposium proposal has been accepted for the upcoming WCCM-ECCOMAS 2026 Congress that will be held in Munich, Germany from 19-24 July 2026! 🎉
``UNIFIED MICROMECHANICS THEORY of Composites” (UMTC) by Buryachenko is proposed for analyses of composite materials (CMs) of either matrix random (statistically homogeneous or inhomogeneous probability descriptors), periodic, or deterministic (neither random nor periodic) structures, endowed with local, weakly nonlocal (higher-grade, higher-order, or micromorphic models), strongly nonlocal (strain-type or displacement-type, peridynamics), or multiphysics phase properties.
The 20th U.S. National Congress of Theoretical and Applied Mechanics (USNC-TAM 2026) will be held on June 21–25, 2026, in Pasadena, California. We are pleased to invite submissions to our mini-symposium:
MS 208 - Data-Driven Approaches for Solid Mechanics
organized by Qizhi He (U of Minnesota), Jiun-Shyan Chen (UC San Diego), WaiChing Sun (Columbia), and Nikolaos Vlassis (Rutgers U).
The 20th U.S. National Congress of Theoretical and Applied Mechanics (USNC-TAM 2026) will be held on June 21–25, 2026 in Pasadena, California. Together with Shaoting Lin, Ruobing Bai, and Berkin Dortdivanlioglu, we are organizing the mini-symposium 403 - Mechanics and Physics of Soft Materials. You are invited to submit an abstract to our mini-symposium by Jan 15, 2026.
https://www.usnctam26.org/home
A PhD position is available in the group of Prof. Stefano Gonella at the University of Minnesota. The position is in the general area of mechanics and physics of solids, with emphasis on mechanical and elastic metamaterials and wave physics.
Current topics of interest include:
1) Topological mechanics and metamaterials
2) Optimization of metamaterials
3) Multiphysics aspects of metamaterials
The University of Vermont (UVM) invites applications for PhD student positions to work with Professors Michael J. Leamy and Jihong Ma as part of the National Science Foundation’s New Frontiers of Sound (NewFoS) Science and Technology Center.
This opportunity focuses on research in acoustic, electroacoustic, and elastic topological insulators and metamaterials, with applications in radio frequency devices, communication systems, and non-traditional computing.
Context
As part of a collaborative project between
1) MSC Software Belgium SA,
2) SIRRIS and
3) University of Liège,
the main objective of the positions will be to develop numerical methodologies in the context of
1) Multi-scale methods
2) Stochastic approaches
3) Machine learning approaches
to predict the response of recycled thermoplastic composites.
PhD opportunity
The Hamilton-Jacobi equation of classical mechanics is approached as a model reduction of conservative particle mechanics where the velocity degrees-of-freedom are eliminated. This viewpoint allows an extension of the association of the Hamilton-Jacobi equation from conservative systems to general Newtonian particle systems involving non-conservative forces, including dissipative ones. A geometric optics approximation leads to a dissipative Schr¨odinger equation, with the expected limiting form when the associated classical force system involves conservative forces.
Postdoc positions are available at Jiangsu University, China. Those possessing PhDs in the area of mechanical engineering and non-destructive evaluation are sought. We are particularly interested in candidates with experience in experimental mechanics, composite materials and structures etc. Also, candidates with experience in machine learning are also welcome for inter-discipline research.
We seek an outstanding PhD candidate to develop AI-ready representations of metamaterial geometries (geometrical material structures). The project will build an atlas of common metamaterial typologies and learn compact latent representations using variational autoencoders, enabling property-driven search, generative design, and optimisation for targeted physical performance.
