Open Post Doc Positions-Purdue Gilbreth Fellowships

I wanted to bring to attention open Purdue Gilbreth Postdoctoral Fellowship Positions.

https://engineering.purdue.edu/Engr/Research/GilbrethFellowships

In particular I am soliciting calls from good candidates in the following area: (please feel free to contact me for a possibility. prior communication is encouraged.)

https://engineering.purdue.edu/Engr/Research/GilbrethFellowships/Researc...

Using Machine Learning to Predict Damage Tolerance in Unstable Systems: a Case of Impacted Batteries and Exploding Materials

Project Description

Unstable systems are defined as systems that respond to applied stimuli in a way that significantly endangers surroundings. This includes nuclear materials, lithium ion batteries, and energetic materials. Significant advances have been made in designing such systems in a way that initial design is safe and performance is guaranteed. However, influence of a damage event such as an impact or extreme vibrations at high temperatures etc. significantly changes the operational landscape of such systems. The proposed work builds upon recent advances made using mechanical Raman spectroscopy experiments by Tomar and co-workers, and mesoscale modeling and stochastics in energy storage materials by Mukherjee and co-workers, to (1) delve into classical non-equilibrium foundations for defining and separating critical vs. non-critical instabilities in such systems using experiments with specific emphasis on Li-ion batteries, (2) designing new breed to sensors that replicate gained knowledge using machine learning to predict influence of instabilities on unstable system operation, and (3) developing new machine learning informed framework that integrates predictive modeling with underlying physics based on knowledge in (1) and (2). Emphasis is on addressing and removing inherent brittleness in machine learning protocols to include problem physics in such important material systems.