:Full-time
:USA-GA-Kennesaw (Global R&T)
:Global R&T
:Standard
:Research & Development
Developing an accurate nonlinear reduced order model from simulation data has been an outstanding research topic for many years. For many physical systems, data collection is very expensive and the optimal data distribution is not known in advance. Thus, maximizing the information gain remains a grand challenge. In a recent paper, Bhattacharjee and Matous (2016) proposed a manifold-based nonlinear reduced order model for multiscale problems in mechanics of materials. Expanding this work here, we develop a novel sampling strategy based on the physics/pattern-guided data distribution.
Advertising the first fully funded PhD position in my group: this position is for the more computationally/mathematically inclined. Goal: method development.
A "general audience" summary of a recent application of our work: https://www.youtube.com/watch?v=cWTWHhMAu7I
Details about the position: https://vacature.beta.tudelft.nl/vacaturesite/permalink/287309/?lang=en
The NUMISHEET conference series is the most significant international conference on the area of the numerical simulation of sheet metal forming processes. Within Numisheet 2020, we are organizing a mini symposium on “Challenges and Opportunities in Forming Aluminum”.
http://jingjieyeo.github.io/positions.html I am happy to announce that the website of the J2 Lab for Engineering Living Materials is now live! We're very excited to get cracking in Jan 2020 at the Sibley School of Mechanical and Aerospace Engineering in Cornell University, and we're hiring one postdoc experienced in multiscale computational simulations to kickstart our lab. Please visit our website for more details!
Title: Modeling and Simulation Engineer
Category: Full-time position in industry
Employer: Schlumberger Technology Corporation
Location: United States, Texas, Sugar Land
Opening Date: 08/01/2019
To apply, please submit your resume and a list of 3 references to JShi2 [at] slb.com.
Introduction
Dear Colleagues and Friends,
In this short course, we will introduce the participants to the latest efforts on data-driven methods for mechanical and material sciences. The course will cover topics on
1. mechanistic data-driven clustering methods, direct and reduced order modeling techniques,
2. physics-informed neural networks, multi-fidelity Gaussian processes,
3. deep material networks and multiscale material failure analysis.
Some benchmarks on nano-polymer composites, polymer matrix composites, additive manufactured alloys will be demonstrated. For more details, please visit the website, http://15.usnccm.org/sc15-005.
Dear colleagues,
Richard Norte and I are looking for a postdoctoral scholar with interest in machine learning and good knowledge on finite element analyses.
This project is focused on computational mechanics in collaboration with a strong group on opto-mechanical devices.
For more information please check the following link:
https://www.academictransfer.com/nl/54445/pd-next-generation-opto-mecha…
1.
To cut a somewhat long story short, I think that I can ``see'' that Machine Learning (including Deep Learning) can actually be regarded as a rules-based expert system, albeit of a special kind.
I am sure that people must have written articles expressing this view. However, simple googling didn’t get me to any useful material.
I would deeply appreciate it if someone could please point out references in this direction. Thanks in advance.
2.
We have one or more postdoc positions available, to be filled immediately, at MIT’s Laboratory for Atomistic and Molecular Mechanics, under the direction of Professor Markus Buehler. We are looking for postdocs in two broad areas, as described below.
Position #1: Materials science modeling
