Eirini Eleni Tsiropoulou
5 months ago
Electrical/Computer Engineering, Computer Science Arizona State University in United States
Degree Level
PhD
Field of study
Computer Science
Funding
No explicit funding details are provided in the post. It is implied that this is a funded PhD position, but stipend amount, tuition coverage, or other financial details are not mentioned.
Deadline
Aug 1, 2026
Country
United States
University
Arizona State University
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Where to contact
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About this position
Funding details
No explicit funding details are provided in the post. It is implied that this is a funded PhD position, but stipend amount, tuition coverage, or other financial details are not mentioned.
What's required
Applicants should have a strong background in Electrical/Computer Engineering, Computer Science, or a related discipline. Solid understanding of machine learning and anomaly detection, with experience in deep learning frameworks such as PyTorch or TensorFlow, is required. Familiarity with hardware architectures, FPGA-based platforms, or hardware-software co-design is desirable. Strong programming and data analysis skills, excellent communication skills, and motivation for interdisciplinary research are expected. Candidates must be based in the United States, with preference for U.S. citizens due to project constraints.
How to apply
Send a CV, a brief research statement, and the contact information of references to [email protected]. Do not send private messages. Prepare your application materials and email them directly to the supervisor.
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Professors
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PROTON Lab at School of Electrical, Computer and Energy Engineering — ASU ECEE is seeking a highly motivated PhD student to pursue research in the area of machine learning for fault-tolerant computing systems. The position offers the opportunity to work on cutting-edge challenges at the intersection of hardware-software co-design, anomaly detection, and system resilience.
The successful candidate will focus on designing and implementing advanced machine learning methods for analyzing hardware and software telemetry under fault conditions.
Research directions include:
- Developing unsupervised and deep learning models to detect anomalous system behaviors.
- Investigating dual-model anomaly detection strategies tailored to different classes of computing architectures (ASICs vs. microprocessors).
- Leveraging telemetry such as privilege transitions, performance counters, and execution traces to quantify robustness of microarchitectures.
- Fusing multi-abstraction data (gate-level, architectural, and software execution traces) using ML-based approaches for improved anomaly detection.
- Designing predictive algorithms that highlight emergent fault propagation patterns not easily captured by deterministic analysis.
Candidate Profile
- Strong background in Electrical/Computer Engineering, Computer Science, or a related discipline.
- Solid understanding of machine learning and anomaly detection, with experience in deep learning frameworks (e.g., PyTorch, TensorFlow).
- Familiarity with hardware architectures, FPGA-based platforms, or hardware-software co-design is desirable.
- Strong programming and data analysis skills.
- Excellent communication skills and motivation for interdisciplinary research.
- Candidates must be based in the United States, with preference for U.S. citizens due to project constraints.
Opportunities
- Join an internationally recognized research program in machine learning for resilient and secure computing systems.
- Access to advanced hardware-software platforms and experimental infrastructures.
- Publish in top conferences and journals in machine learning, computer engineering, and system reliability.
- Benefit from ASU’s strong collaborative research environment and mentoring.
Interested applicants should send a CV, a brief research statement, and the contact information of references at [email protected]
No PMs, please.
Strict start date: Spring 2026