Post-doctoral position in machine learning approaches for CFD
Please find our new post-doc opening in the http://www.legato-team.eu
Write to Dr Anina GLUMAC <anina . glumac @ uni.lu> to apply.
Check the group out here:
machine learning
One PhD position in multiscale simulation and characterization of polymer composites at Howard University, Washington, DC
The Sustainable Infrastructure, Geotechnics, and Materials (SIGMa) Lab at Howard University invites applicants for a Ph.D. position in multiscale simulation and characterization of polymer composites starting January 2022. Highly motivated individuals with strong computational, machine learning, microscopy and communication skills are encouraged to apply.
Responsibilities
Lecturer/Senior Lecturer/Reader Position in Multiscale Modelling at the University of Glasgow-James Watt School of Engineering
We are now seeking to fill an academic position at Lecturer/Senior Lecturer/Reader rank for a successful academic to join the Division of Systems, Power and Energy Engineering. We specifically seek a candidate who can potentially contribute to the James Watt School of Engineering in the area of modelling of materials and/or processes at lower length scales where continuum assumption ceases to be valid.
Webinar: Simpleware R-2021.03 Release: Highlights of New and Improved AI-Powered Tools
Check out this webinar on the AI-enabled segmentation and landmarking tools in the latest release of Simpleware software - ideal if working with orthopedic and cardiology medical image data and you want to speed up tedious segmentation.
Registration is here: https://register.gotowebinar.com/register/2163352213172894476?source=IM…
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:
Mechanistic Machine Learning and Digital Twins for Computational Science, Engineering & Technology (MMLDT-CSET 2021)
An IACM Conference September 26-29, 2021 - Hyatt Regency Mission Bay, San Diego, USA
(website: https://mmldt.eng.ucsd.edu/)
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'.
Data-driven 2 Ph.D. positions: fracture, large deformation, machine learning, phase field, polymers in Computational Mechanics in Luxembourg - industry-academia partnership - SnT
2 Ph.D. positions in Computational Mechanics in Luxembourg
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:
https://authors.elsevier.com/a/1bxlSAQEIt1ls
The article focuses on how to systematically create constitutive laws using only:
1) Available microstructural models
2) Machine learning techniques