Postdoctoral Position in Additive Manufacturing Process Simulation at SUSTech, China
Postdoctoral Position in Additive Manufacturing Process Simulation at SUSTech, China
Position Information
Postdoctoral Position in Additive Manufacturing Process Simulation at SUSTech, China
Position Information
cdmHUB invites you to attend the Global Composites Experts Webinar Series.
Title: A semi-discrete model for progressive damage and failure of fiber reinforced composites.
Speaker: Dr. Anthony M. Waas
Time: 11/11, 11AM-12PM EST.
Please go to https://bit.ly/2ZGRTl3 to register for this webinar.
The Department of Aerospace Engineering and Engineering Mechanics at the University of Texas at Austin is ranked #8 best graduate program by US News. We currently have a tenure-track position in the area of Experimental Nano/Micro-Mechanics beginning Fall 2022. The deadline to apply is 12/01/2021. Please apply or spread the word for us.
See this URL for the most up-to-date information: https://tinyurl.com/scilab-postdoc-2021
In March 2021, The University of Manchester signed a 10 year collaboration agreement with the UKAEA, resulting in a £15M investment for two new research groups; Digital Engineering and Tritium. I lead the Digital Engineering group. The aim is to build a virtual garage where engineers can try out new designs for fusion reactors more quickly than building full-scale prototypes.
Fully supported Postdoc and Ph.D. positions are available immediately in Dr. Kai Yu’s research group (https://sites.google.com/site/kaiyuhomepage/) at the University of Colorado Denver.
Hi everyone, I am a postdoc at Yale University. I am posting on some of my previous work on fiber networks and heterogeneous materials. We reported that effective stiffness is a concave function of inhomogeneity properties, and effective stiffness becomes smaller with increasing heterogeneity as an example consequence.
Our new paper on ultrasonic vibration fatigue [a collaboration between 4 universities & 3 countries] is available for free access until December 21, 2021,
Wei Gao
Department of Mechanical Engineering, University of Texas at San Antonio
In this journal club, we provide a brief summary on the concept, recent progress and tools of machine learning (ML) potential for atomistic materials modelling. We hope that it could benefit to the readers who are new to this filed and plan to develop their own or use others ML potentials. Comments and disscussions are welcomed.