Ph.D. Opportunity: Building Edge AI for Real-Time 3D Mapping and Autonomous Sensing

About the Project

Location: University of Nottingham, Faculty of Engineering

Start date: 1 October 2026

Application deadline: 24 July 2026

Project type: Collaborative Academic–Industry PhD studentship

Industrial partner: BAE Systems plc

Academic supervisors: Dr Sendy Phang and Dr George Gordon

Industry supervisor: Dr Hassan Zaidi

We are seeking a Ph.D. student to develop next-generation AI systems for real-time 3D mapping on compact, low-power devices. The project will combine optical sensing, event-based vision, and radio-frequency (RF) data with advanced AI to build robust mapping systems for challenging environments, including poor visibility and GPS-denied settings.

This is a joint project with BAE Systems plc, offering access to industrially relevant datasets, equipment, and evaluation scenarios alongside academic research training. It would suit candidates interested in careers in academia or industry, especially in AI, sensing, autonomy, robotics, or embedded systems.

Background

Accurate 3D mapping is increasingly important for autonomy, navigation, inspection, and situational awareness across defence and other safety-critical applications. Yet many real-world deployments cannot depend on cloud computing or high-bandwidth communications. Instead, sensing and AI inference must operate directly at the edge, under tight constraints on power, bandwidth, and compute. This studentship addresses that challenge by developing a multimodal sensing and inference framework that can run on compact AI edge hardware while remaining reliable in complex, contested, or visually degraded environments.

Aim

You will design, build, and evaluate a hardware-aware AI framework for cognitive 3D mapping. The work will bring together three complementary sensing streams:

• structured illumination for active optical depth recovery and high-precision 3D sensing;
• event-based vision for low-latency, high-dynamic-range perception with reduced data rates;
• RF sensing and localisation, spanning radar-style observables and passive RF localisation using software-defined radio.

A central theme of the project is co-design across sensing, AI reconstruction, and embedded deployment. You will explore how multimodal models can generate consistent 3D scene representations with quantified uncertainty, and how these can be deployed efficiently on edge accelerators such as NVIDIA Jetson, Edge TPU, or neuromorphic hardware.

What we offer

• A world-class interdisciplinary research environment spanning photonics, sensing, AI, nanotechnology, and engineering.
• A supportive and inclusive research culture, underpinned by the Researcher Development Concordat (http://www.vitae.ac.uk/policy/concordat).
• Supportive supervision from academic and industrial partners.
• Opportunities to publish in leading journals and conferences and present work internationally.
• Four years of funding (tuition fees + stipend) at the standard rate for eligible UK students.
• A consumables budget for state-of-the-art edge AI compute units and sensors.
• A project environment suited to careers in academia, advanced R&D, or industry innovation.

What you should have

Applicants should ideally have:

• a first-class or upper second-class degree, or a master’s degree, in Engineering, Computer Science, Physics, Mathematics, Robotics, or a related discipline;
• interest in AI, machine learning, computer vision, signal processing, sensing, robotics, or embedded systems;
• programming experience in Python, MATLAB, C/C++, or similar;
• strong analytical and problem-solving skills;
• the ability to work independently and within a multidisciplinary academic–industry team;
• eligibility for Home fee status.

How to apply

For informal enquiries and application details, contact Dr Sendy Phang at sendy.phang@nottingham.ac.uk with your CV, a cover letter outlining your research interests and motivation for the project, academic transcripts, and any publications.

Funding Notes

Open only to Home Fee status students (i.e. UK or settled EU) - nationality restrictions may also apply.