PhD Studentship: Electronic Reliability and Asset Management for Application in Printed Circuit Board Design Test and Maintenance

Applications are invited for a PhD degree in Artificial Intelligence and Machine Learning (AI/ML) to advance the security and reliability of cyber-physical systems, embedded systems, and electronics/electrical systems. The research would focus on building advanced machine learning techniques and edge AI modelling for moving to the next generation of secure and reliable electronic networking specific to applications in the automotive industry.

The advancement of technology has led to the widespread use of electrical and electronic systems in various industrial applications. Although such technological advances have created a large market for printed circuit boards (PCB) and electrical component suppliers, they open new challenges for industries and users concerning design, manufacturing, maintenance, and life-cycle management. The problem is more acute in applications requiring secure, safe, durable, robust, and reliable systems.

Evaluating the performance of complex systems from various aspects of the electrical and electronic system requires deploying integrated and multi-domain processes for automating tests and assessments. It involves building tools for failure diagnosing and advancing system maintenance by developing effective prognoses policies, which should be applied to PCB repair and maintenance.

With this research, you will study state-of-the-art PCD design, test and assessment. Further, you would contribute to the electrical, thermal, structural and fatigue analysis of electronic and electrical components and systems and explore artificial-Intelligent techniques for analyzing the test results from a wide range of system performance and optimization, ultimately leading to advancing PCB life-cycle management.

The successful culmination of this project envisages the availability of an efficient and intelligent life-cycle test and management regime for highly future innovative, reliable- and safety-critical PCBs. It enhances the design and maintenance of electronics over its entire design, manufacturing and operational service life. Also, the AI-based built-in solutions in this domain would serve as a benchmark for high-end platforms requiring electronics and PCBs, for intense, in the marine, commercial, military, and automotive sectors.

At a glance

Application deadline: 20 Aug 2023

Award type(s): PhD

Start date: 29 Sep 2023

Duration of award: 3 years

Eligibility: UK, EU, Rest of World

Reference number: SATM320

Entry requirements

Applicants should have a first or second class UK honours degree or equivalent in a related discipline.

This project would suit:

Graduate and post-graduate students with a degree in engineering (preferably in electrical, electronic, mechanical or computation), data science, or any other related physical sciences subject.

Researchers and Engineers with a background/interest in automotive, electronics and electrical systems evaluation concerning reliability and certification. PhD candidates must have software programming skills (intermediate to advanced level) and familiarity with AI and Machine Learning methods.

Above all, research aspirants with innovative approaches, high motivation and willingness to learn are encouraged to apply.

Funding

This studentship opportunity is open to applicants in the UK, EU and International. There are no restrictions on nationality. The PhD is self-funded.