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Ph.D position is available for Fall 2023 on mechanics of mechanical metamaterials, bio-inspired engineering, and additive manufacturing (3D/4D printing). Candidates with background in mechanics of materials, Finite element analysis, and mechanical experiments are all welcome to apply. Earlier admission (starting from the summer) is also possible. It is a position in the department of Mechanical and Industrial Engineering at Northeastern University, Boston.

Postdoc positions are available in Dr Min Luo’s group (https://person.zju.edu.cn/en/minluo) at the Ocean College of Zhejiang University, China (ranked #45 in QS Global World Rankings 2022). Those holding a PhD degree in the areas of Computational Fluid Mechanics and Civil/Hydraulic/Ocean Engineering are welcome to apply.

1.    Project description

We are looking for a highly motivated postdoctoral researcher to join our team in the Department of Mechanical Engineering at the University of North Carolina at Charlotte. The ideal candidate will have a Ph.D. in Mechanical Engineering, Civil Engineering, Aerospace Engineering, Computer Science, or a related field, with expertise in the area of machine learning and (computational) solid mechanics.

We are looking for a highly motivated postdoc to join our team to study the mechanics of woven or fiber-based materials. Applicants should have expertise in mechanics of materials with long fibers. These can be artificial, like textiles and fabrics, or natural, like soft tissues including tendon or heat valves.  Experience in multi-scale methods is advantageous.  Experimental or computational are both welcome.

https://www.amazon.com/Introduction-Unified-Mechanics-Theory-Applicatio…

I would like to share our recent paper entitled "Numerical investigation of aerothermoelastic characteristics of a thin heated panel in high supersonic and hypersonic flow" published in Acta Mechanica.

I am looking for a PhD student to join my group - Computational Physics and Machine Learning - at Aarhus University to work on the ERC project ALPS - AI-based Learning for Physical Simulation. The goal of the research is to develop a new generation machine learning and simulation algorithms that are able to automatically build interpretable models of physical systems starting from experimental data. These algorithms will be applied in the contexts of biological systems, human health, fluid dynamics and engineering.

In this webinar, we will introduce the latest analysis technologies with specific analysis examples to predict materials behavior using analytical prediction of microstructure combined with virtual material testing.

Save the date: 22 February 2023 at 10 AM CET ⇒ Register from here

Webinar Abstract