º£½ÇºÚÁÏ

Building edge AI for real-time 3D mapping and autonomous sensing  

We are seeking a PhD 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:

1. structured illumination for active optical depth recovery and high-precision 3D sensing
2. event-based vision for low-latency, high-dynamic-range perception with reduced data rates
3. 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.

Project environment

The project will be based in the Faculty of Engineering at the º£½ÇºÚÁÏ with Dr Sendy Phang and Dr George Gordon as the academic supervisors. You will benefit from a research culture that combines hands-on systems development with advanced AI methods, alongside co-supervision and strategic input from BAE Systems through industry supervisor Dr Hassan Zaidi.

PhD start date: 1 October 2026. 

What we offer

Joining our team means gaining access to exceptional resources and opportunities to develop you into a leading researcher.

• A world-class research environment spanning sensing, nanotechnology, AI, and clinical medicine
• A supportive and inclusive research culture underpinned by the
• Close technical supervision from both academic and industrial partners to work on a real-world industry problem
• Excellent opportunities to publish in leading journals and conferences, to present your work internationally, and travel to conferences
• A project environment well suited to students interested in careers in academia, advanced R&D, or industry innovation

Candidate requirements 

We are seeking a motivated candidate with the enthusiasm and technical foundation to contribute to ambitious interdisciplinary research. You 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
• A strong interest in one or more of the following areas:
  • AI and machine learning
  • computer vision
  • signal processing
  • sensing
  • robotics
  • embedded systems
• Programming experience in at least one language such as Python, MATLAB, or C/C++
• Strong analytical, quantitative, and problem-solving skills
• The ability to work effectively both independently and as part of a multidisciplinary academic–industry team

Funding and eligibility

This studentship is open to home fee status candidates only.

• Four years of funding including tuition fees and stipend at the standard rate for eligible UK students
• Consumables budget for purchasing state-of-the-art edge AI compute units and sensors

How to apply

For informal enquiries and details on how to apply please email Dr Sendy Phang at sendy.phang@nottingham.ac.uk with your:

• CV
• Cover letter outlining your research interests and motivation to do this PhD project
• All academic transcripts
• Any publications