Research Assistant / Senior Research Assistant

About the Job
The Research Assistant will work on the development of a GenAI-powered cyber twin for power-sector critical infrastructure and the design and evaluation of mitigation strategies against cyber-physical attacks. The role involves building and refining the twin generation engine, defining and measuring the sufficiency metric against iTrust's EPIC testbed, and developing the mitigation generator and validator modules that propose, test, and iteratively refine defensive strategies on the cyber twin.

What You'll Do

• Assist in the development of the cyber twin for the EPIC testbed, covering process data, control logic, SCADA configuration, and OT network architecture.
• Support the definition and measurement of fidelity and sufficiency metrics by running paired experiments on both the cyber twin and the physical testbed.
• Develop automated capabilities that propose candidate mitigation strategies based on observed attack outcomes on the cyber twin.
• Develop automated capabilities that deploy, evaluate, and iteratively refine candidate strategies until they meet a defined confirmation criterion.
• Support the proof of concept demonstrating the cyber twin's readiness for use in international cyber range exercises.
• Contribute to research publications and project documentation.

Who We're Looking For

• Bachelor's or Master's degree in Computer Science, Computer Engineering, Electrical Engineering, Cybersecurity, or a related discipline.
• Foundational knowledge of machine learning or deep learning concepts, with willingness to learn GenAI and large language model techniques.
• Programming proficiency in Python.
• Basic understanding of industrial control systems (SCADA, PLC, RTU) or willingness to learn.
• Interest in cybersecurity, particularly in the context of critical infrastructure and operational technology.
• Good written and verbal communication skills.
• Exposure to digital twin concepts, simulation environments, or process modeling is a plus.
• Coursework or projects involving graph neural networks, prompt engineering, or retrieval augmented generation is a plus.
• Familiarity with power-sector protocols such as IEC 61850 and GOOSE is a plus.