Job Details

Qualifications:
PhD in Electrical Engineering, Mechanical Engineering, Nuclear Engineering, or other related engineering discipline; must be completed by commencement of appointment and within the previous 5 years.
Excellent theoretical and practical knowledge of control systems
Ability to architect, implement, and maintain artificial intelligence / machine learning-based automation control systems, in particular, anticipatory control systems
Must be able to diagnose and prognose automation problems collaboratively with team members.
Ability to integrate and demonstrate developed autonomous control platform to control the performance of an experimental testbed
Knowledgeable to develop control algorithms in one or more of the following: C/C++, MATLAB/Simulink (preferred) or National Instruments LabVIEW.
Ability to learn new techniques and apply them to current and future projects is highly desired.
Experience with hybrid modeling approach would be a plus
Experience in multi-physics modeling and simulating tools such as Multiphysics Object Oriented Simulation Environment (MOOSE), COMSOL, and/or other relevant tools would be a plus.
Strong publication record
Excellent oral and written communication skills
Ability to contribute to new idea development and/or proposal development
Able to mentor undergraduate and graduate interns
Strong personal and interpersonal skills are essential
Ability to perform research independently