Irfan Khan
3 weeks ago
Funded PhD Position in PLC Security, LLM-Based Detection and Correction, and Cyber-Physical Systems Texas A&M University in United States
Degree Level
PhD
Field of study
Computer Science
Funding
At least one, and possibly two, funded PhD studentships are available. The post states the position is funded, but no stipend amount, tuition details, or duration are provided.
Country
United States
University
Texas A&M University

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About this position
PhD position opening in the Clean and Resilient Energy Systems (CARES) Lab at Texas A&M University for research on PLC security, Large Language Models (LLMs), and cyber-physical systems.
The project, Enhancing PLC Security: LLM-Based Detection and Correction, focuses on using LLMs to automatically identify vulnerabilities in Programmable Logic Controller (PLC) code and generate secure fixes. This is a strong fit for students interested in Electrical Engineering, Computer Science, cybersecurity, industrial automation, SCADA, and critical infrastructure.
The supervisor is Irfan Khan, Assistant Professor and Chair IEEE GBS Section. The post says he is recruiting at least one, and possibly two, funded PhD students to start in Spring 2027.
Preferred background includes ECE, CS, or a closely related field; hands-on experience with LLMs, prompt engineering, or model fine-tuning; familiarity with cybersecurity and PLC/SCADA systems; and proficiency in Python plus deep learning frameworks such as PyTorch or TensorFlow.
To apply, email [email protected] with the subject line Prospective PhD Applicant - PLC Security. Include a CV/resume, a brief statement of relevant experience, and unofficial transcripts.
Funding details
At least one, and possibly two, funded PhD studentships are available. The post states the position is funded, but no stipend amount, tuition details, or duration are provided.
What's required
Applicants should have a strong academic background in ECE, Computer Science, or a closely related field. Desired experience includes Large Language Models, prompt engineering, or model fine-tuning, plus familiarity with or strong interest in cybersecurity, cyber-physical systems, and industrial automation such as PLCs and SCADA. Proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow is expected.
How to apply
Email the supervisor directly at [email protected] with the subject line 'Prospective PhD Applicant - PLC Security'. Attach your CV/resume, a brief statement of experience with AI/LLMs or cybersecurity and PLCs, and unofficial academic transcripts.
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