Project Overview
Connected & Autonomous Vehicles will transform the future of transport mobility. However, proving that CAV technologies are safe remains a key barrier in their introduction. Our research focuses on the safe and robust functionality of Advanced Driving Assistance Systems (ADAS) and Automated Driving Systems (ADSs) before commercial application can be realised. The complex nature of such systems requires development of both innovative methods of evaluation and the development of dynamic test scenarios.
The student will be involved in creating a model-based safety analysis for ADAS and ADS. The student would be working on a system architecture (and not just chip design). Understanding the system requirements and its influence on system on chip design and IP level components (HW and SW) to allow for a seamless integration process.
In this PhD position, the student will work closely with:
- Verification & Validation (V&V) of Intelligent Vehicles at WMG, University of Warwick which is the leading international role model for successful collaboration between academia and the public and private sectors, driving innovation in science, technology and engineering, to develop the brightest ideas and talent that will shape our future on Connected & Autonomous Vehicles
- The Central Technology Solutions Functional Safety team at ARM and will join a growing and highly motivated team responsible to develop Functionally Safe architectures for state-of-the-art projects within autonomous drive systems, digital cockpit and robotics. Arm’s ecosystem includes many of the biggest names in consumer electronics and semiconductor manufacturing.
Essential and desirable criteria
The ideal candidate will be a self-starter with a strong desire to pursue a career in hardware design or software engineering. They will have some programming experience.
· Motivation to pursue and complete a PhD program with industrial partners
· Desire to contribute to the research in the field of automation.
· A master’s degree in computer science or Electronic/Electrical/Computer Engineering or other similar degree
· You can collaborate and communicate well within an engineering team
· You have the willingness to take risks and collaborate with colleagues to create solutions to new problems
· Experience with programming languages (Python, C, C++)
· Experience with hardware description languages (VHDL, Verilog, System)
· Experience with computer architecture
Desirable:
- Academic knowledge of a subset of the following subjects:
- Dependability focused on Cybersecurity, Safety and Reliability
- Industrial or Automotive or Aerospace systems engineering
- Functional safety analysis methodology such as: System Theoretic Process Analysis (STPA), Fault Tree Analysis (FTA), Failure Mode Effect Analysis (FMEA) and Dependent Failure Analysis (FTA)
- Model-Based System Engineering (MBSE)
· Machine learning and large data analysis is a plus
To know more about a PhD journey, you may find some details here: https://bit.ly/PhivePsOfPhD
Prospective candidates are expected to have a minimum 2.1 undergraduate (BEng, MEng, BSc, MSci) and/or postgraduate masters’ qualification (MSc) with 65% or above.
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