Position Summary:
- Fundamental research in functional safety improvement through expanded diagnostic coverage and reduction in the testing effort: Study safety-critical applications and how to achieve Safety Integrity Level (SIL) to reduce the risk to an acceptable level. Study black-box regression testing of software in safety-critical systems. Also, study the two approaches – increasing redundancy and improving diagnostic coverage and their capability to meet SIL. Develop Machine Learning techniques for increasing testing efficiency.
- Fundamental research in hybrid strategies for AI-based safety assurance: study the shortcoming in safety prediction and assurance accurately when using data-driven models alone. Also, develop a hybrid approach by combining data-driven approaches with the physics of failure approaches.
- Fundamental research in functional safety verification of systems that implement AI: Develop AI tools according to current functional safety standards to carry out safety-critical functions. Also, study and improve the transparency in data and analytical methods for AI-based safety analysis.
Minimum Qualifications:
- Masters degree in Engineering, Physical Sciences, Mathematics, or Computer Science
- Experience with execution of research projects with a track record of publications
Application Documents:
- Resume
- Cover Letter
- List of References
To apply for the position, send the documents to Dr. Michael Azarian and Dr. Diganta Das.
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