Evaluation and Hardening of Embedded AI Modules for Safety and Security in Critical Systems

Grenoble INP - LCIS; France; Sur place; EUR 40 000 - 60 000; Plein temps

Résumé du poste

A leading research institution in France is offering a PhD position focused on developing methodologies for improving the robustness of AI in embedded systems. This role involves conducting experiments related to safety and security in autonomous technologies, requiring candidates to hold a Master's degree in a relevant field. Join a project that addresses critical challenges in embedded AI modules used in sectors like healthcare and automotive.

Qualifications

• Master's degree in a relevant field is required.
• Experience with AI and embedded systems is essential.
• Strong understanding of safety and security for AI modules.

Responsabilités

• Develop evaluation methodology for embedded AI robustness.
• Conduct experiments to validate hardening techniques.
• Integrate various safety-related techniques for autonomous systems.

Connaissances

Embedded Systems

Computer Science

Cybersecurity

Artificial Intelligence

Prototyping and Simulation

Formation

Master's in Embedded Systems

Master's in Computer Science

Master's in Microelectronics

Master's in Cybersecurity

Master’s in AI

Description du poste

Organisation/Company Grenoble INP - LCIS Research Field Computer science » Informatics Engineering » Electronic engineering Researcher Profile Recognised Researcher (R2) Leading Researcher (R4) First Stage Researcher (R1) Established Researcher (R3) Country France Application Deadline 5 Feb 2026 - 22:00 (UTC) Type of Contract Temporary Job Status Full-time Offer Starting Date 1 Jan 2026 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No

Offer Description

The growing integration of Artificial Intelligence (AI) modules into safety-critical embedded systems (autonomous vehicles, drones, industrial and medical devices) raises major safety and security concerns. These modules, often based on deep neural networks, are sensitive to both accidental faults and intentional attacks [1]-[5] that can alter their decisions. Ensuring their robustness under real-world conditions is therefore essential for trustworthy deployment. Current approaches mainly focus on software-level adversarial robustness or high-level fault tolerance.

This PhD aims to develop a unified methodology for evaluating and improving the robustness of embedded AI modules against various real-world and physical disturbances, encompassing both safety-related faults and security-related attacks. The work will: (1) Identify, model, and reproduce representative perturbations that may cause abnormal or unsafe behavior. (2) Evaluate their effects on performance, safety, and security metrics. (3) Propose and validate mitigation and hardening techniques at the model, system, and learning levels.

The targeted application will concern multi-sensors based systems for autonomous vehicles embedding AI perception or decision modules. The experiments will rely on embedded platforms available at LCIS, including electromagnetic fault injection benches. The methodology will address both safety-related disturbances (accidental faults) and security-related threats (intentional perturbations), highlighting the differences between them in embedded AI systems.

Approach and Methodology

1. Definition of robustness metrics combining accuracy, integrity, latency, and safety.
2. Formalization of realistic fault and attack scenarios at different system levels and integration (real-world, sensors, preprocessing chain, AI module).
3. Implementation and validation of representative fault and attack scenarios, using a combination of simulation and physical experimentation on embedded platforms, to evaluate the robustness of AI modules and the effectiveness of the proposed hardening techniques.
4. Cross-layer analysis of robustness techniques, evaluating interactions and complementarities between mitigation methods for different classes of disturbances (to avoid conflicting protections or redundant efforts).
5. Design and validation of new efficient countermeasures across system levels, developing and experimentally assessing strategies under embedded constraints.

Expected Outcomes

• A methodology for robustness and fault-injection evaluation of embedded AI modules.
• Experimentally validated hardening strategies improving both safety and security.
• A benchmark and reproducible framework to support future studies for improving cross-layer robustness techniques.
• Design recommendations for safe and secure integration of AI in critical embedded systems.

References

• [1] M. Dumont, K. Hector, P.-A. Moellic, J.-M. Dutertre, et S. Pontié, "Evaluation of Parameter-based Attacks against Embedded Neural Networks with Laser Injection", International Conference on Computer Safety, Reliability, and Security (2023), doi: 10.48550/ARXIV.2304.12876.
• [2] V. Moskalenko, V. Kharchenko, et S. Semenov, "Model and Method for Providing Resilience to Resource-Constrained AI-System", Sensors, vol. 24, no 18, p. 5951, janv. 2024, doi: 10.3390/s24185951.
• [3] A. Bosio, P. Bernardi, A. Ruospo and E. Sanchez, "A Reliability Analysis of a Deep Neural Network", 2019 IEEE Latin American Test Symposium (LATS), Santiago, Chile, 2019, pp. 1-6, doi: 10.1109/LATW.2019.8704548.
• [4] P. Rech, "Artificial Neural Networks for Space and Safety-Critical Applications: Reliability Issues and Potential Solutions", in IEEE Transactions on Nuclear Science, vol. 71, no. 4, pp. 377-404, April 2024, doi: 10.1109/TNS.2024.3349956.
• [5] S. Burel, A. Evans and L. Anghel, "Zero-Overhead Protection for CNN Weights", 2021 IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFT), Athens, Greece, 2021, pp. 1-6, doi: 10.1109/DFT52944.2021.9568363.

PhD Student Profile

• Master's in Embedded Systems
• Master's in Computer Science
• Master's in Microelectronics
• Master's in Cybersecurity
• Master’s in AI
• Computer Architecture
• ML-based AI
• Prototyping and Simulation of Digital Systems