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Our data science, AI, health data science and business analytics programmes have launched the careers of hundreds of data professionals over the last 10 years. Students from our programmes have progressed to data science roles at Amazon, PWC, Ernst & Young, Hawaiian Airlines, eBay, Zurich Insurance, the Co-operative Group, N Brown, the NHS and many others - please look at our Education pages for further details of the courses on offer.

PhD Overview

As society becomes increasingly reliant on digital infrastructure, it is critical that decision-makers at organisational and national levels understand the resilience of their systems. Analysts use Attack Surface Mapping (ASM) to identify their internet-connected digital assets and associated vulnerabilities. This allows them to understand how robust the infrastructure is, plan mitigation strategies, and support recovery post-attack.

This PhD will leverage data-science, AI, and cybersecurity techniques to develop the next generation of ASM tools. Research will include:

• Fusing multiple ASM tools and pieces of open-source information to give more accurate understanding of attack surfaces than the current state-of-the-art tools can provide.
• Developing techniques to measure and interpret the uncertainty of ASM results, giving practitioners confidence in their analysis.
• Investigating how AI automation can safely and effectively improve the ASM process.

This PhD is in collaboration with SP Electricity North West, with a crucial focus on securing digital infrastructure across their network and enabling the secure deployment of innovative new services as they digitise their operations. Furthermore, this project aligns with ongoing work the team are carrying out with the UK’s National Cybersecurity Centre (NCSC); as such, there is a real opportunity for your research to make an impact.

Supervisory Team

• Dr Edward Austin(School of Computing and Communications)
• Professor Nicholas Race(School of Computing and Communications)
• Dr Xiandong Ma (School of Engineering)

Training and Development

The successful candidate will receive a tailored training programme including:

• Support using, and access to, ASM tools such as Shodan and Censys.
• Opportunities to engage with national and international conferences, workshops, and training events.
• Insight into the power sector through industrial collaboration with SP ENWL.

Funding

• A 3.5-year UKRI-funded studentship, including a stipend (currently £20,780 per year) and full tuition fees for Home students.
• An additional research training grant (£1,000 per year) for consumables, maintenance, and travel to events/conferences.

Eligibility

• Applicants should have (or expect to obtain) a First or Upper Second-Class degree (or equivalent) in Computer Science, Data Science, or Cybersecurity. Applicants from other disciplines with a substantial mathematical component are also encouraged to apply.
• There is no expectation that a candidate will be proficient in all areas of data science, cybersecurity, computer networking and AI tooling. However, candidates should be aware that this PhD will have a substantial cybersecurity component.

Application Process

Applicants should submit:

1. A cover letter outlining their motivation and suitability.
2. A CV outlining skills and experience.

Applications will be considered on a rolling basis until the position is filled. The expected start dates are either April 2026 or October 2026.

Contact Information

Please contact Professor Nicholas Race (n.race@lancaster.ac.uk) and Dr Edward Austin (e.austin@lancaster.ac.uk)

Project Overview

Teleoperation is a critical enabler for safe and efficient operation in hazardous environments such as nuclear decommissioning. However, current industrial solutions suffer from limitations under uncertainty, time delays, and noisy sensing.

This PhD project will design and experimentally validate a hybrid motion–force control framework that ensures precise end-effector positioning while maintaining robust and adaptive force regulation under real-world conditions. Research will include:

• Development of nonlinear robust adaptive controllers and disturbance observers.
• Design of bilateral teleoperation schemes that enhance transparency and stability under communication delays.
• Integration of data-driven approaches for force estimation and safety.
• Experimental validation on industrial robotic platforms at the UKNNL Hot Robotics Facility.

The project provides the opportunity to work on cutting-edge robotics challenges with significant industrial impact, supported by state-of-the-art facilities at both Lancaster University and UKNNL.

Supervisory Team

• Dr Allahyar Montazeri (Lead Supervisor, School of Engineering, Lancaster University; Data Science Institute Member)
• Prof Plamen Angelov (Co-Supervisor, School of Computing and Communications, Lancaster University; Data Science Institute Member)
• Dr Naomi Rutledge Industrial co-supervisor

Training and Development

The successful candidate will receive a tailored training programme including:

• Hands-on training with ROS2, MATLAB/Simulink, and CoppeliaSim.
• Access to world-class robotics laboratories and facilities.
• Opportunities to engage with national and international conferences, workshops, and training events.
• Insight into the nuclear sector through industrial collaboration with UKNNL.

Funding

• Duration: 4 years (3.5 years EPSRC Doctoral Landscape Award + 0.5 years UKNNL extension)
• Coverage: UKRI minimum stipend, tuition fees for Home students, and a research training support grant.
• Additional support for consumables, maintenance, and travel.

Eligibility

• Open to UK Home students only, due to clearance requirements for UKNNL facilities.
• Applicants should have (or expect to obtain) a First or Upper Second-Class degree (or equivalent) in Engineering, Control, Robotics, Computer Science, or a related discipline.
• Strong mathematical and programming skills (MATLAB, Python, or C++) are highly desirable.

Application Process

Applicants should submit:

1. A full CV.
2. A one-page cover letter outlining their motivation and suitability for the project.
3. Reference letter from two academics commenting on the candidate abilities.

Applications will be considered on a rolling basis until the position is filled, with an expected start date of April 2026.

For informal enquiries, please contact Dr Allahyar Montazeri a.montazeri@lancaster.ac.uk

Application deadline 1st December

Questions? Contact Julia Carradus j.carradus1@lancaster.ac.uk