PhD Studentship: Assured Runtime Control for Safe Autonomy (ARCS-A) (PhD in Engineering (Funded))
University of Exeter
Modern autonomous ground vehicles (AGVs/UGVs) in defence operations require sophisticated AI/ML-based control systems for perception, decision-making, and adaptive responses in complex, unstructured environments where terrain can change abruptly. However, formally certifying these opaque learning-based components demands impractical resources, presenting critical safety assurance challenges and delaying the adoption of novel technologies. Our prior independent and DSTL-funded research has established foundationa...
Modern autonomous ground vehicles (AGVs/UGVs) in defence operations require sophisticated AI/ML-based control systems for perception, decision-making, and adaptive responses in complex, unstructured environments where terrain can change abruptly. However, formally certifying these opaque learning-based components demands impractical resources, presenting critical safety assurance challenges and delaying the adoption of novel technologies. Our prior independent and DSTL-funded research has established foundationa...
Opportunity details
Modern autonomous ground vehicles (AGVs/UGVs) in defence operations require sophisticated AI/ML-based control systems for perception, decision-making, and adaptive responses in complex, unstructured environments where terrain can change abruptly. However, formally certifying these opaque learning-based components demands impractical resources, presenting critical safety assurance challenges and delaying the adoption of novel technologies. Our prior independent and DSTL-funded research has established foundational safety assurance techniques for autonomous systems: This PhD will develop an assured runtime safety controller designed to enable autonomous systems to operate safely in dynamic environments. Developed in collaboration with SC Group Ltd., with applications to defence autonomous systems, the approach combines multiple techniques such as onboard safety monitoring, operating environment adaptation and real-time robust learning of uncertainties and nonlinearities within the dynamical system. In this PhD, the aim is to simultaneously learn control policies and safety certificates—mathematical proofs that control decisions are safe. Data from system operation provides evidence that both the control and the proofs are valid. The proposed controller will prevent unsafe actions during the deployment of AI/ML-enabled functional blocks in the closed-loop control of AGVs/UGVs.
Eligibility and requirements: Review the official provider page for the current applicant conditions and required documents.
Funding and benefits
Funding eligibility: UK Students. Funding amount: UK tuition fees and an annual tax-free stipend of at least £25,000 per year.
How to apply
Developed in collaboration with SC Group Ltd., with applications to defence autonomous systems, the approach combines multiple techniques such as onboard safety monitoring, operating environment adaptation and real-time robust learning of uncertainties and nonlinearities within the dynamical system.
Open the official application information
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Source and verification
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Recorded source check: October 2, 2026. A stored check date does not guarantee that every field is complete or still current. Compare the provider, eligibility, funding and deadline with the live official call before applying.
Benefits
Funding eligibility: UK Students. Funding amount: UK tuition fees and an annual tax-free stipend of at least £25,000 per year.
Eligibility summary
Requirements
Documents required
Language/test requirements
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