Position Opening

Currently, I have a number of openings for highly motivated Ph.D. students and/or Postdoctoral scholar to join my research group at the University of Nevada, Reno. A high-level description of prospect projects and required background is provided below. Domestic candidates and candidates from underrepresented minorities are highly encouraged to apply. Please send me your complete C.V., if you meet the qualifications (email to hebrahimian@unr.edu).

Research Theme 1 – Integration of Models with Data / Data Assimilation and Stochastic Filtering

The prospect research will be focused on developing practical methods for data assimilation, Bayesian inference, model updating, and machine learning 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, and Undergraduate GPA > 3.5 and Graduate GPA > 3.75. Moreover, you should have a solid background and/or interest in 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.
Bayesian statistics and applied mathematics.
Model inversion, parameter estimation, and optimization.
Neural networks and artificial intelligence.

Research Theme 2 – Simulation, Monitoring, and Remote Diagnosis of Offshore Wind Turbines

To be eligible, you should have an education in Civil or Mechanical Engineering, and Undergraduate GPA > 3.5 and Graduate GPA > 3.75. Moreover, you should have relevant knowledge and work or research experience in wind turbines. This theme will have significant overlap with the Research Theme 1. A solid background and/or interest in the following subjects is required:

Modeling and response simulation of offshore wind turbines. Experience with OpenFAST is a plus.
Rotordynamics and applied mechanics.
Computer programming – extensive experience with Matlab, Python, and/or C++. Experience with cloud computing and GPU-based computing is a plus.
Machine health monitoring and fault diagnosis.
Methods for structural sensing, monitoring, and system identification.
Random vibrations.
Stochastic signal processing.
Bayesian statistics, applied mathematics.
Model inversion, parameter estimation, and optimization.
Neural networks and artificial intelligence.
Data analysis.

Research Theme 3 – Wildfire Simulation and Risk Assessment

The prospect research will be focused on computational methods for wildfire modeling, data assimilation, and risk assessment. To be eligible, you should have an education in Engineering or related fields (Undergraduate GPA > 3.5 and Graduate GPA > 3.75) with a background and/or interest in the following subjects:

Fire or wildfire simulation.
Computer programming – extensive experience with Linux, Fortran, Matlab, and/or Python. Experience with cloud computing is a plus.
Reliability and risk engineering.
Methods for data assimilation and uncertainty quantification.
Remote sensing.
Neural networks and artificial intelligence.