Position Openings

We are recruiting talented Postdoctoral Scholars (from any field) and/or Ph.D. Students (with an engineering background). Please review the project details and required qualifications below. If you meet the qualifications, send me your complete C.V. (email to hebrahimian@unr.edu).

Research Theme 1 – Wildfire Simulation, Data Analytics, and Risk Engineering
(Ph.D. Students / Postdoctoral Scholars)

The prospect research will be focused on wildfire engineering, including simulation, data analytics, risk assessment, and resilience engineering. You need to be a smart and motivated self-learner, interested in learning new technical concepts, solving abstract problems, learning new software, and developing codes.

 

Postdoctoral candidates need to have relevant experience and research background with a history of quality publications.

 

Ph.D. candidates should have an undergraduate degree in engineering, undergraduate GPA > 3.5, graduate GPA > 3.75, and a solid background in one or more of the following subjects:  

  • Computer programming – past experience with Matlab, Python, and/or C++.

  • Experience with cloud computing, GPU-based computing, and a working knowledge in Linux.

  • Machine learning, neural networks, and artificial intelligence.

  • Spatial data analysis.

  • Computer vision techniques.

  • Statistics, probability, reliability, and engineering risk assessment.

  • Methods for stochastic simulation and uncertainty quantification.

  • Resilience engineering and disaster sciences.  

  • Fire sciences.

Research Theme 2 – Structural Mechanical, Computational Modeling, Data Methods
(Ph.D. Students)

The prospect research will be within the general areas of experimental research, structural mechanics, computational modeling, data methods with application to civil and mechanical systems. You should be good at theoretical research, solving mathematical problems, and developing and implementing large-scale computer codes.

 

To be eligible, you should have an education in Civil or Mechanical Engineering, undergraduate GPA > 3.5, graduate GPA > 3.75, and a solid background in one or more of the following subjects:

  • ​​Theoretical knowledge in nonlinear computational mechanics and nonlinear FE modeling. Experience with a simulation platform such as OpenSees and/or LS-DYNA is required. 

  • Computer programming – extensive experience with Matlab, Python, and/or C++. Experience with cloud computing and GPU-based computing is a plus.

  • Methods for structural sensing, monitoring, and system identification.

  • Random vibrations.

  • Stochastic signal processing. 

  • Model inversion, parameter estimation, and optimization.

  • Neural networks and artificial intelligence.

  • Computer vision techniques.