Topic

Machine learning-based approaches for modeling and numerical simulation of advanced materials.

Preferred background

Bachelor and/or Master Degrees in Mechanics and/or Material Science.
Strong multidisciplinary background with interest and experience in:
Machine learning, Artificial intelligence, Deep learning;
Material characterization, Microstructure analysis, Mechanics of materials;
Computer programming, Parallel and/or distributed computing.

To apply

Recently, there has been a growing interest in investigating the multi-functional properties of novel types of metamaterials, metastructures, and metacomposites based on triply periodic minimal surfaces (TPMS). TPMS have been discovered more than 160 years ago and thanks to additive manufacturing (3D printing) they become reality only recently and opend the doors for several applications.

How to provide a structure with a force lying on a fixed line?
We provide the answer to this question in our paper: Bigoni and Misseroni (2019) Structures loaded with a force acting along a fixed straight line, or the “Reut’s column problem”.
Journal of the Mechanics and Physics of Solids.
Enjoy watching our video below (would you have trouble playing video on YouTube, click here to watch it).

A PhD student is sought to perform research on systems engineering theory relying on the mathematics of probability theory, decision theory, game theory and social choice theory.  The student will be supported under an NSF grant in the Mechanical Engineering department of George Mason University and work with Dr. George Hazelrigg.  If interested, please contact Dr. Hazelrigg at ghazelri [at] gmu.edu (ghazelri[at]gmu[dot]edu).

How a mass falls when attached to a flexible element with varying length?
We provide the answer to this question in our paper: Armanini, Dal Corso, Misseroni, Bigoni (2019) Configurational forces and nonlinear structural dynamics.
Journal of the Mechanics and Physics of Solids.
Enjoy watching our video below (would you have trouble playing video on YouTube, click here to watch it).

May a catastrophe machine be realized through an elastic continuous element?
We provide a positive answer to this question in our paper: Cazzolli, Misseroni, Dal Corso. Elastica catastrophe machine: theory, design and experiments. Journal of the Mechanics and Physics of Solids.

Enjoy watching our videos below (would you have trouble playing videos on YouTube, click here to watch them).

Mechanical mismatches between implanted electronics and biological tissues can lead to inaccurate readings and long-term tissue damage. Here, we show that functionalized multi-walled carbon nanotubes twisted into helical fibre bundles that mimic the hierarchical structure of muscle can monitor multiple disease biomarkers in vivo. The flexible fibre bundles are injectable, have a low bending stiffness and display ultralow stress under compression.

The HetSys team are recruiting its second cohort of enthusiastic students from across the physical sciences who enjoy using their mathematical skills and thinking flexibly to solve complex problems to join our new EPSRC Centre for Doctoral Training in Modelling of Heterogeneous Systems at the University of Warwick, UK. Our exciting range of PhD projects can be found on our webpage.

We are looking for an Academic Associate to join the Department of Engineering.  This post is part-time and in addition you will be required to register for a PhD part-time for the duration of the 5-year post. Part-time PhD fees will be paid by the Department for 5 years and the PhD will be in the area of 3D printing of Smart Shape Memory Materials for Advanced Sensors and Actuators.

PhD Title: 3D printing of Smart Shape Memory Materials for Advanced Sensors and Actuators

Director of Studies: Dr. Mahdi Bodaghi

Duration: 5 years, part time.

Measuring forming limit diagrams (FLDs) is a time consuming and expensive process. Machine learning (ML) methods are a promising route to predict FLD of aluminium alloys. In the present work, we developed a machine learning (ML) based tool to establish the relationships between alloy composition / thermomechanical processing route to the material's FLD.