Research Studentship in Autonomous Robotics: Multi-collaborative scouting and mapping

4-year DPhil studentship 

Supervisor:Prof Ioannis Havoutis 

Project: Multi-collaborative scouting and mapping with a team of highly mobile robots

About the RAINZ CDT

This studentship is offered by the EPSRC Centre for Doctoral Training in Robotics and Artificial Intelligence for Net Zero (RAINZ CDT) which is a partnership between three of the UKs leading universities (The University of Manchester, University of Glasgow and University of Oxford).

Robotics and Autonomous Systems (RAS) is an essential enabling technology for the Net Zero transition in the UK’s energy sector. However, significant technological and cultural barriers are limiting its effectiveness. Overcoming these barriers is a key target of this CDT. The focus of the CDT’s research projects will be how RAS can be used for the inspection, maintenance, and repair of new infrastructure in renewables (wind, solar, geothermal, tidal, hydrogen) and nuclear (fission and fusion), and to support the decarbonization of existing maintenance and decommissioning of assets.

We are seeking talented and motivated graduate scientists and engineers who are eager to learn new skills and have a desire to help increase use of RAS to help decarbonise the energy sector. Your work will help foster innovation and drive meaningful change in this increasingly important area of science and engineering.

Year 1: You will spend the first year of the CDT at The University of Manchester undertaking taught MSc studies and research training. You must achieve an average of 65% or higher in your MSc taught assessments to be considered for progression to the PhD studies.

Note: you will not graduate with an MSc. If you meet the progression criteria, you will transition directly onto the PhD.

Years 2 – 4: You will move to your host institute (University of Oxford) to undertake your PhD research, which will be complimented with a comprehensive cohort training and employability development programme.

About the Project

This PhD project will focus on developing an AI-based system for multi-collaborative scouting and mapping using a team of highly mobile legged or legged-wheeled robotic platforms. The research will investigate advanced algorithms for multi-robot coordination, dynamic path optimization, and collaborative exploration to enable efficient mapping of unknown environments. Emphasis will be placed on leveraging SatCom connectivity and heterogeneous sensor data and real-time decision-making to adapt to complex terrains and environmental challenges. By combining secure connectivity, mobility, adaptability, and collective intelligence, this work aims to create robust and scalable solutions for rapid area exploration, with applications in search-and-rescue, environmental monitoring, and planetary exploration.

Eligibility

This studentship is funded through the EPSRC and an industrial partner. It is open to Home students (full award – home fees plus stipend). This project is subject to funding being confirmed by the industry partner (European Space Agency (ESA) & European Centre for Space Applications and Telecommunications (ECSAT)).

Award Value

Course fees are covered at the level set for Home students c. £9,500 p.a. The stipend (tax-free maintenance grant) is c. £20,780 p.a. for the first year, and at least this amount for a further three years. 

Candidate Requirements

Prospective candidates will be judged according to how well they meet the following criteria:

• A first class or strong upper second-class undergraduate degree with honours (2:1 with 65% average), or international equivalent, Engineering, Computer Science, Physics or Mathematics
• Excellent English written and spoken communication skills
• Evidence of programming experience

Application Procedure

Please apply via RAINZ course page.

Informal enquiries are encouraged and should be addressed to Prof Ioannis Havoutis (ioannis.havoutis@eng.ox.ac.uk).

Candidates must submit a graduate application form and are expected to meet the graduate admissions criteria. Details are available on the course page of the University website.

Application deadline: noon on 11 July 2025

Start date: October 2025