A postdoctoral and graduate student openings with the main focus on the mechanics of composites materials are available immediately in Shakiba's group. We are looking for strongly motivated candidates to work on an AFOSR supported project on 1) thermo-mechanical damage coupling in FRPs, 2) simulation of additively manufactured composites and 3) sensitivity and machine learning for damage predictions.
Hiring four (4) new PhD students to join us at MACHINA: the new TUDelft AI lab that I am leading.
Each position is unique and has different co-advisors. Pick wisely ;)
https://doi.org/10.1039/D0TB00896F It is my privilege and honor to be highlighted as the Journal of Material Chemistry B's Emerging Investigators for 2020. Together with our group's young budding scientists, Chenxi Zhai, Tianjiao Li, and Haoyuan Shi, we review the discovery and design of next-generation bio-inspired materials by harnessing the virtual space in materials design: materials omics (materiomics), materials informatics, computational modelling and simulations, artificial intelligence (AI), and big data.
Inline virtual qualification from 3D X-ray imaging for high-value manufacturing
2.5-year Research Software Engineer (RSE) opportunity, Closing date: 18 June 2020.
https://ibsim.page.link/RSE-Jun20
This research opportunity is a joint project with partners Diamond Light Source and UK Atomic Energy Authority (aka CCFE).
Postdoc Open Position
Postdoc position with financial support (up to 3 years) is immediately available in the Department of Mechanical Engineering, University of Connecticut (UConn).
Required Degree:
Ph.D. in Engineering Mechanics, Chemical, Mechanical, or Civil Engineering, Material Science, Condense Matter Physics, or Computational Chemistry.
Required Skills:
Designing complex architectured materials with generative adversarial networks
Yunwei Mao, Qi He, Xuanhe Zhao
Two postdoctoral positions are available in the Department of Mechanical Engineering at the University of British Columbia (UBC), Vancouver Campus. The positions are described below and involve the use of large-scale ab-initio simulations in combination with machine learning models to accelerate materials discovery. The positions are funded for two-years and available in the modelling and simulation group, lead by Prof. Mauricio Ponga.
Safer batteries, more efficient gas-turbine engines and solar cells, all require better-engineered nanocomposite materials. There is a limitation though -- how to investigate the fracture mechanics of these materials? Machine learning can help us overcome this limitation. Read more in our just-published paper: https://lnkd.in/e7dPBtx
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.
