machine learning

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.

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…

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/)

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:

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

 2 Ph.D. positions in Computational Mechanics in Luxembourg 

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

https://computationalengin.blogspot.com/2020/11/stage-edfminesafh-paris-region-advanced_3.html