uncertainty quantification

                                                                               OPENINGS FOR A POST-DOCTORAL RESEARCHER

 in

Computational Mechanics Research Laboratory (CMRL)

Johns Hopkins University

Professor Somnath Ghosh

Come and join us in Prague to become a 2023 CTU Global Postdoctoral Fellow!

Czech Technical University in Prague offers a fully-funded two-year postdoctoral position in Data-driven modeling of engineering materials. The project will be mentored by Anna Kučerová and will involve collaboration with Bořek Patzák within the MuPIF software project.

One postdoc is sought to fill an opening in the Gross Materials Lab (group website: andrewjgross.com) at the University of South Carolina. This research is focused on extracting yield surfaces from data rich full-field experimental information by solving an inverse problem. This research will be primarily computational and will integrate closely with a graduate student conducting experiments. Applicants with relevant experience are strongly encouraged to apply.

The Uncertainty Quantification (UQ) Lab at the University of Houston (UH), led by Dr. Ruda Zhang, invites applications for:

• two (2) PhD student positions in areas of data-driven engineering and uncertainty quantification, and
• one (1) postdoctoral researcher in areas of surrogate modeling and data-driven dynamics.

For job details and updates, see lab webpage: https://uq.uh.edu/positions

 PhD Students - 2023

In 2016, our colleague (Prof. Ahmad Elbanna) addressed one such extreme event (namely, earthquakes and the associate rupture physics). In this journal article, I will be focusing on two other extreme events, namely tsunamis and storm surges and some of the modelling efforts to address these coastal hazards.

Dear Members of the Mechanics and Materials Communities,

You may have known about the efforts of the Mechanics of Materials and Structures (MoMS) program of the National Science Foundation on revitalizing the practice of verification and validation (V&V) of computational models as a rigorous and indispensable step in the scientific investigative process.

Esteemed Colleagues,

Dear Colleagues,

As part of the IMECE 2020 (November 13-19, 2020, Portland, Oregon), we are organizing a topic on “Data-Enabled Predictive Modeling, Machine Learning, and Uncertainty Quantification in Computational Mechanics.” It is listed in Track 12: Mechanics of Solids, Structures, and Fluids: https://event.asme.org/IMECE/Program/Tracks-Topics.

We've recently published our new study about Uncertainty Quantification in Molecular Dynamics (MD) Simulations. Due to the selection of functional forms of interatomic potentials or the numerical approximation, MD simulations may predict different material behavior from experiments or other high-fidelity results. In this study, we used Stochastic Reduced Order Modeling (SROM) to achieve

(1) mechanical behavior of graphene predicted by MD simulations in good agreement with the continuum model which has been calibrated by experiments;

Dear colleagues,

We encourage you to submit your abstracts to minisymposium 1801 of the 13th World Congress on Computational Mechanics (New York City, from July 22 to 27 of 2018). This minisymposium focuses on:

1. recently developed methods for data-driven approaches;

2. data-driven applications to fluids, structures and materials involving (but not limited to) machine learning, uncertainty quantification and/or optimization.

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.

Reference : Accelerating Monte Carlo estimation with derivatives of high-level finite element models
http://orbilu.uni.lu/handle/10993/28618

Bayesian inference for the stochastic identification of elastoplastic material parameters: Introduction, misconceptions and insights http://orbilu.uni.lu/handle/10993/28631

Context

Profs. Thierry J. Massart (Universite Libre de Bruxelles), Ludovic Noels (Universite de Liege) and Stephane P.A.
Bordas (University of Luxembourg) have recently been awarded a joint research project by the FNRS and FNR.
The project focuses on the mechanical behavior of discrete metallic materials, such as metal foams and printed metallic structures

PhD opportunities

The Computational Mechanics group at Georgia Southern University, led by Dr. Xuchun Ren, is looking for a new Research Assistant, who are capable of and interested in performing high-quality research on engineering design. The research, supported by  U.S. National Science Foundation and Startup Funding, entails building a solid mathematical foundation, devising efficient numerical algorithms, and developing practical computational tools for stochastic topology optimization.

The Computational Mechanics group at The University of Iowa, led by Professor S. Rahman, is looking for two new Ph.D. students, who are capable of and interested in performing high-quality research on isogeometric methods and uncertainty quantification. The research, supported by U.S. National Science Foundation, requires building a solid mathematical foundation, devising efficient numerical algorithms, and developing practical computational tools, all associated with stochastic isogeometric analysis of complex materials and structures. A substantial background in solid mechanics with coding experience in finite-element or similar methods is a must; exposures to uncertainty quantification and probabilistic methods are highly desirable.

We are pleased to announce the Advanced Short Course at the International Centre for Mechanical Sciences (CISM) on

Identification Methods for Structural Health Monitoring and Residual Lifecycle Assessment

June 3, 2013 — June 7, 2013

http://www.cism.it/courses/C1303/

http://www.ibk.ethz.ch/ch/news/CISM.pdf

Dear Colleagues,

We kindly invite you to submit an abstract to the minisymposium:

“Methods and Applications for Experimental Design with Uncertainties”,

which we are organizing at the 12th U.S. National Congress on Computational Mechanics (USNCCM12) Raleigh, North Carolina, July 22 – 25, 2013.

We are seeking applicants for a Postdoctoral Fellowship in “stochastic multiscale analysis and design” in the Department of Mechanical Engineering, Northwestern University, Evanston, IL, USA. Applicants are expected to have expertise in the following areas: solid mechanics, uncertainty quantification, finite element modeling, and engineering optimization. The candidate is expected to have a strong background in statistical analysis. Prior experience with material microstructure characterization via image processing, and model uncertainty quantification is preferred.

Modern composite structures have a wide spread in their failure stress.  Advanced multiphysics codes can have a wide range of predicted behavior for nominally the same inputs.  How do we certify the design of such structures or the accuracy of such codes?