Fully funded, four year PhD positions in Materials Science and Engineering available at Tampere University, Finland.
Application deadline: 29th Oct 2020 (apply as soon as possible!)
Refer: https://tuni.rekrytointi.com/paikat/?o=A_RJ&jgid=3&jid=723 for more details and to apply for these positions.
Interested students can apply here . A multi-year fully funded PhD research assistantship in the Department of Engineering Science and Mechanics is available. The project focuses on in situ monitoring of microstructure variation and residual stress during the powder bed fusion additive manufacturing process.
EML Webinar on 14 October 2020 will be given by Prof. Howard Stone, Princeton University; Manouk Abkarian and Simon Mendez, CNRS, France.
Title: Fluid dynamics of speech: Mechanisms underlying pathogen transmission
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The department of Mechanical Engineering at the University of Utah is hosting a Virtual Open House for prospective graduate students and we'd love for you to attend! We will be going over general program information, how to apply, and how to finance your degree. We’ll also have a faculty and student panel and virtual live lab tours! This is a great opportunity for you to learn more about our program and see if it's a good fit for you! Register below (space is limited due to Zoom capabilities). |
Multiple fully funded PhD positions are available in the group of Francesco Maresca (https://www.rug.nl/staff/f.maresca/) at the University of Groningen, Netherlands, in Multi-scale mechanics modelling of materials, with focus on:
1. Hydrogen Embrittlement in Steels;
2. Solute strengthening in High Entropy Alloys.
Bioinspired hybrid materials from spray-formed ceramic templates, Dwivedi, Flynn, Resnick, Sampath, Gouldstone, Advanced Materials, 2015
Novelty/impact/significance:
Different from most research that explore exquisite fabrications, an industrial thermal spray-based technique is developed for making nacreous brick-and-mortar alumina composites, which allows for controlled architecture across length scales, diverse constituent materials, and easy scaling-up.
This paper describes a data-driven approach to predict mechanical properties of auxetic kirigami metamaterials with randomly oriented cuts. The finite element method (FEM)was used to generate datasets, the convolutional neural network (CNN) was introduced to train these data, and an implicit mapping between the input orientations of cuts and the output Young’s modulus and Poisson’s ratio of the kirigami sheets was established. With this input–output relationship in hand, a quick estimation of auxetic behavior of kirigami metamaterials is straightforward.
Dear Colleagues,
This is the preprint of an article on the design of elastomer networks for optimal electromechanical response that will appear in JMPS. We explore how various structural properties of an elastomer network (e.g. density of cross-links, fraction of loose-end monomers, orientation density of chains, etc.) affects both its bulk elastic and dielectric properties, and its performance as an actuator. (https://doi.org/10.1016/j.jmps.2020.104171).
