1 Ph.D. position (36 months) within the research activity related to the PI ERC Consolidator Grant "SFOAM" will be funded by and available at the University of Trento (Italy).
The financial conditions offered with this Ph.D. position are in line with those of all Italian Ph.D. schemes, designed to be compatible with the cost of living in Italy, where medical care is free and accommodation costs are reasonable.
Multiple postdoc and one PhD positions are open at Tsinghua University. The research will take place at Tsinghua University’s Shenzhen International Graduate School (SIGS), located in Shenzhen, China, and is partly sponsored by NSFC and Tsinghua SIGS’s scientific research startup funds.
The Multiscale Modeling of Materials and Machine Learning Laboratory (M4L Lab: https://www.m4l-lab.com/) at Villanova University is looking for two Ph.D. students. The successful applicants will work with Dr. David Cereceda on interdisciplinary research topics that involve Materials Informatics and Computational Mechanics. The positions start as early as Fall 2023. Evaluations will start immediately until the positions are filled. Please, see the description of each position below.
Position 1
Join the Ansys Customer Excellence team to partner with our customers to engineer what's ahead, solve their real-world engineering problems, deploy Ansys software in their design workflows, and grow Ansys’ business. As a hands-on subject matter expert, you will use expert-level engineering knowledge to provide technical advise, perform professional services, and help translate customer requirements into exciting new product features.
Our group in the Department of Mechanical and Industrial Engineering at New Jersey Institute of Technology (NJIT) is working on a range of problems, involving mechanics and physics of 2D materials, soft materials and biological systems. We employ methods of continuum mechanics, mathematics, statistical physics and atomistic simulations to explore the mechanics of 2D materials, electromechanical coupling in soft materials, as well as physical mechanisms underlying the interface of nanomaterials and biology.
Dear iMechanicians:
Oxford University is seeking to appoint a Professor of Materials Engineering (Chair) in the department of Engineering Science.
This post is likely to be of interest to high profile, established researchers in the field of mechanics of materials. The successful candidate will be expected to embrace the opportunity to shape the direction of Materials Engineering at Oxford and to pursue a research programme at the forefront of the subject, providing research leadership to colleagues working in this field.
We have a fully-funded position for a PhD student in the EMSI lab, in mechanical engineering at EPFL, and are seeking qualified applicants. The thesis topic is: Crack front stability in 3D. Information about our lab can be found on our website at: Engineering Mechanics of Soft Interfaces ‐ EPFL
What we seek:
My colleague Jan Novák is searching for a postdoctoral researcher to work with him on leveraging the potential of Wang tilings in sharing microstructures of heterogeneous materials.
This position is fully funded for two years and is offered as a part of the 2023 CTU Global Fellowship program. Additional details on the offer and the application process are available at https://euraxess.ec.europa.eu/jobs/118832, and the call closes on 31 August 2023.
Come and join us in Prague to become a 2023 CTU Global Postdoctoral Fellow!
Czech Technical University in Prague offers a fully-funded two-year postdoctoral position in Data-driven modeling of engineering materials. The project will be mentored by Anna Kučerová and will involve collaboration with Bořek Patzák within the MuPIF software project.
I have an opening in my group for a 2-year postdoc position offering applicants an exciting opportunity to join my ERC-funded project “ALPS - AI-based Learning for Physical Simulation”.
Job description
You will be contributing to developing novel algorithms at the intersection of computational physics and machine learning for the automatic identification of symbolic models of physical systems starting from experimental data.
