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PhD Candidate in Advanced Physical Security of Embedded Systems in Norway

मालिक: NTNUI-GJØVIK - NORGES TEKNISK- NATURVITENSKAPELIGE UNIVERSITETS IDRETTSFORENING I GJØVIK
स्थान: Norway
पद: 1
दर्ज की तिथि: 2026-09-04

काम में क्या करना होगा

This is NTNUNTNU is a broad-based university with a technical-scientific profile and a focus in professional education. The university is located in three cities with headquarters in Trondheim. At NTNU, 9,000 employees and 43,000 students work to create knowledge for a better world. You will find more information about working at NTNU and the application process here. Video: https://youtu.be/Xt-yHCN5QS0About the positionAre you motivated to take a step towards a doctorate and open up exciting career opportunities? As a PhD Candidate with us, you will work to achieve your doctorate, and at the same time gain valuable experience that qualifies you for a further career in higher education and research, in and outside academia. Department of Information Security and Communication Technology (IIK) at NTNU Gjøvik is seeking a highly motivated candidate for a full-time PhD position in Advanced Physical Security of Embedded Systems. You will become part of an active and interdisciplinary cybersecurity research environment and have the opportunity to collaborate with researchers working across hardware security, embedded systems, cybersecurity, and digital forensics. A significant part of the research will be carried out in the Hardware security and Reverse Engineering Laboratory (HRE Lab), providing the candidate with access to advanced experimental infrastructure and opportunities to contribute to the further development of the laboratory’s research and educational activities. You will work in a collaborative research environment with opportunities for scientific publications, international collaboration, conference participation, and development of hands-on experimental expertise. Your immediate leader will be the Head of Discipline. About the projectModern embedded systems increasingly rely on cryptographic implementations and hardware-supported security mechanisms to protect sensitive information, trusted operations, and critical functionality. While cryptographic algorithms and software-level protections may be theoretically secure, their physical implementation can introduce vulnerabilities that are not visible at the algorithmic level. Sensitive information may leak through power consumption, electromagnetic emissions, timing behaviour, memory or bus activity, while manipulation of voltage, clock, or electromagnetic conditions can cause security mechanisms to behave in unintended ways. At the same time, modern embedded architectures increasingly integrate processors, memories, peripherals, hardware accelerators, security components, and multiple clock and power domains. Interactions between these elements may introduce new and insufficiently understood physical attack surfaces, creating a need for more systematic methods to evaluate and assure the physical security of embedded systems. This PhD project aims to advance physical security evaluation and hardware security assurance for embedded systems by combining advanced side-channel analysis, fault-injection techniques, AI- and machine-learning-assisted analysis, robustness evaluation, and quantitative security assessment. The research will investigate how physical vulnerabilities arise, how to systematically discover and characterise them, and how to evaluate their practical security impact. This will include established and emerging physical attack mechanisms such as power and electromagnetic side channels, voltage/clock/EM fault injection, and combined attacks, as well as leakage and fault behaviour associated with timing and clock mechanisms, memory and buses, peripherals, hardware accelerators, cryptographic implementations, and interactions between architectural domains. AI and machine learning will be explored as enabling technologies for automated leakage detection and localization, analysis of complex measurements, fault-response characterization, intelligent exploration of large experimental spaces, and adaptive selection of security experiments. The research will be