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WaiChing Sun's picture

MS Invitation "Multiscale Machine Learning for Geotechnical and Geophysics and Geomechanics Applications: from Grain- to Field-scales" @IACM conf. MMLDT-CSET 2021

Dear Colleagues,

We would like to invite you to submit an abstract to the symposium on Multiscale Machine Learning for Geotechnical and Geophysics and Geomechanics Applications: from Grain- to Field-scales at the MMLDT-CSET 2021 conference, September 26-29:

PhD opening in Computational Mechanics and Optimisation at University of Technology Sydney, Australia

The project “Computational mechanics and optimisation for energy absorption of materials” is calling for applications for PhD scholarship commencing in 2021. This project is funded by the Australian Research Council Discovery Early Career Researcher Award. Candidates with knowledge and research experience in computational mechanics, design optimisation and machine learning are encouraged to apply.


Project description:

Call for Papers: Special Issue on Advances in Integrated Digital Engineering Applications

Dear Colleagues,

I'm current guest editor on a Special Issue of the MDPI Journal of Applied Sciences. This is a journal with an impact factor of 2.474.

The aim of this Special Issue is to explore the re-engineering of engineering through the integration of advanced digital technologies. Research papers or case studies involving any discipline of engineering are welcome. Topics may include, but are not limited to, the following:

University Lectureships in Mechanics and Materials (x 2) - Cambridge

Applications are invited for two University Lectureships in Mechanics and Materials Engineering, to be known as the 'Granta Design University Lectureships in Engineering'.

Julian J. Rimoli's picture

New article on inelastic homogenization through machine learning

I would like to share another article from my group just published in CMAME. I think this is an interesting approach towards the automatic generation of constitutive laws for arbitrary microstructures:

The article focuses on how to systematically create constitutive laws using only:

1) Available microstructural models

2) Machine learning techniques

cbrinson's picture

New AI + Metamaterials postdoc position

We are seeking a creative and enthusiastic postdoc to work at Duke University as part of a collaborative new DOE funded project entitled "FAIR Data and Interpretable AI Framework for Architectured Metamaterials". Candidate should have phd in mechanical or materials engineering, with experience in mechanical metamaterials, the underlying physical principles, finite element simulations, and preferably some knowledge of AI/ML and desire to learn more.

Fully funded PhD position on machine learning for scientific computing at Stony Brook University

A fully supported PhD position with an emphasis on machine learning and scientific computing is available in the Civil Engineering Department at Stony Brook University under the guidance of Dr. Georgios Moutsanidis. The desired start date is Fall 2021. 


mshakiba's picture

Post-doctoral research associate and Graduate student openings

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.

mbessa's picture

Four (4) PhD positions in AI @ TU Delft

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 ;)


Jingjie Yeo's picture

Discovery and design of soft polymeric bio-inspired materials with multiscale simulations and artificial intelligence 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.

Llion Evans's picture

Research Software Engineer (RSE) position

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.

Ying Li's picture

Multiple Postdoc and PhD Open Positions in Polymer Modeling and Machine-Learning at University of Connecticut

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.

mponga's picture

Two postdoctoral positions available at UBC

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.


Machine Learning for Fracture Mechanics

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.

Nuwan Dewapriya's picture

Characterizing fracture stress of defective graphene samples using shallow and deep artificial neural networks

Abstract: Advanced machine learning methods could be useful to obtain novel insights into some challenging nanomechanical problems. In this work, we employed artificial neural networks to predict the fracture stress of defective graphene samples. First, shallow neural networks were used to predict the fracture stress, which depends on the temperature, vacancy concentration, strain rate, and loading direction.

danialfaghihi's picture

Mini-symposium on “Data-Enabled Predictive Modeling, Machine Learning, and Uncertainty Quantification in Computational Mechanics” at IMECE 2020 conference

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:


Mirkhalaf's picture

PhD position on machine learning enhanced multi-scale modelling of textile composites at the University of Gothenburg

We have an open PhD position on machine learning enhanced multi-scale modelling of textile composites. The following link provides more information about the project, and the details of the application process. Please keep in mind that only applications sent through the online application system will be evaluated.

Description of the PhD project, and how to apply


mbessa's picture

Journal Club for February 2020: Machine Learning in Mechanics: simple resources, examples & opportunities

Machine learning (ML) in Mechanics is a fascinating and timely topic. This article follows from a kind invitation to provide some thoughts about the use of ML algorithms to solve mechanics problems by overviewing my past and current research efforts along with students and collaborators in this field. A brief introduction on ML is initially provided for the colleagues not familiar with the topic, followed by a section about the usefulness of ML in Mechanics, and finally I will reflect on the challenges and opportunities in this field.

mbessa's picture

[Deadlines updated] ICTAM2020 & WCCM2020

Dear colleagues,

Deadline to submit your abstract to ICTAM2020 and WCCM2020 is fast approacing (January 20 & 15, respectively). If you are working with machine learning, uncertainty quantification, optimization or a related topic, consider the following symposia:


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