A prestigious, fully funded 3.5-year PhD at Cranfield University focusing on AI-driven, resilient cognitive navigation systems for autonomous transport. Sponsored by EPSRC DLA, Cranfield, and Spirent Communications, this project contributes directly to the UK’s Net Zero mobility goals.
Important Information
| Field | Details |
|---|---|
| Title | Ubiquitous Cognitive Navigation with AI Based Systems – PhD Studentship |
| Organization/Publisher | Cranfield University |
| Work Location | Cranfield, United Kingdom |
| Research Field | Autonomy, Navigation, Artificial Intelligence, Positioning & Sensor Fusion |
| Funding Info | Fully Funded (Home/UK students only) |
| Application Deadline | 08 April 2026 |
| Country | United Kingdom |
| Researcher Profile | PhD Researcher |
| Apply Button | Apply via Cranfield University’s official online portal |
| Required Qualification | First or Second-class Honours degree (UK) or equivalent |
| Required Experience | Programming (Python/MATLAB), robotics, aerospace, embedded systems, sensor fusion |
| Salary / Stipend Details | £24,000 per year tax-free stipend + full tuition fees |
| Duration | 3.5 years |
| Start Date | 01 June 2026 |
| Reference Number | CRAN-0028 |
| Eligibility | UK/Home students only |
Overview
Cranfield University invites applications for a fully funded PhD studentship in Ubiquitous Cognitive Navigation using AI-Based Systems, supported by the EPSRC Doctoral Landscape Awards (DLA), Cranfield University, and Spirent Communications. This interdisciplinary project aims to revolutionize the future of autonomous transport by developing intelligent, resilient Position, Navigation and Timing (PNT) systems capable of operating reliably—even when satellite signals are degraded, jammed, or denied.
As the UK moves toward greener, more autonomous mobility solutions, the ability to maintain safe and accurate navigation becomes crucial. This PhD directly supports the country’s Net Zero transport strategy, contributing to safer, more efficient, and sustainable intelligent mobility systems.
Project Overview
Autonomous systems rely heavily on accurate navigation inputs. However, GNSS-dependent systems are vulnerable to:
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Signal interference
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Jamming
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Urban canyons
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Environmental degradation
This research proposes an AI-driven cognitive navigation framework capable of adaptively fusing data from multiple sensors under uncertainty. Key focus areas include:
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Machine learning-driven sensor fusion
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Cognitive architectures for adaptive navigation
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PNT resilience in challenging or GPS-denied environments
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Use of radio signals, inertial sensors, and novel signal sources
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Real-time decision-making and uncertainty management
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Hardware-in-the-loop testing
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Software-defined radio (SDR) integration
The outcome will be next-generation navigation architectures for autonomous transport and intelligent mobility systems.
Industry Collaboration
This project is co-sponsored by Spirent Communications, a global leader in navigation testing technologies. Candidates will benefit from:
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Two industry placements
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Access to state-of-the-art simulators and datasets
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Industry mentoring and technical support
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Opportunities for real-world system testing
Training & Development
The PhD offers wide-ranging training in:
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AI and machine learning
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Autonomous navigation
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Sensor fusion
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SDR & hardware-in-the-loop simulation
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Technical writing and project management
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Conference presentations and public engagement
Students join the Cranfield Doctoral Network—a vibrant, multidisciplinary community delivering seminars, workshops, collaboration events, and skills development.
Diversity & Inclusion
Cranfield University strongly champions equity, diversity, and inclusion. The programme welcomes applicants from:
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Underrepresented groups
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Individuals with disabilities or neurodiversity
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Diverse ethnic, gender, cultural, and socioeconomic backgrounds
The university actively supports an inclusive research culture through initiatives such as Athena SWAN, Working Families, WES, and international diversity programmes.
Entry Requirements
Applicants should possess:
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A first or second-class UK honours degree (or equivalent) in:
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Aerospace
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Robotics
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Computer Science
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Electrical/Electronic Engineering
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Autonomous Systems
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Experience with:
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Python/MATLAB
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Sensor fusion or machine learning
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Robotics or embedded systems
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Signal processing
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Enthusiasm for autonomous systems and hands-on experimentation
Funding Information
The studentship includes:
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£24,000 annual tax-free stipend
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Full tuition fee coverage
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Eligibility restricted to Home/UK students
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Applicants must have no restrictions on UK residency duration
How to Apply
Eligible applicants should submit an online application via Cranfield’s official portal.
Contact for further information:
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Name: Dr Mengwei Sun
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Email: Mengwei.sun@cranfield.ac.uk
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Phone: +44 07594 731903
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