About us

We are working to create a world-class Data Science Institute at Lancaster (DSI@Lancaster) that sets the global standard for a truly interdisciplinary approach to contemporary data-driven research challenges. DSI@Lancaster aims to have an internationally recognised and distinctive strength in being able to provide an end-to-end interdisciplinary research capability - from infrastructure and fundamentals through to globally relevant problem domains and the social, legal and ethical issues raised by the use of Data Science.

The Institute is initially focusing on the fundamentals of Data Science including security and privacy together with cross-cutting theme areas consisting of environment, resilience and sustainability;health and ageing, data and society and creating a world-leading institute with over 300 affiliated academics, researchers, and students.

Our data science, 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.

ExaGEO, a partnership between Lancaster University (CEEDS), the University of Glasgow, and the University of Edinburgh (EPCC), offers fully funded PhDs in geo- and computer sciences, statistics, and computational engineering. ExaGEO will deliver the next generation of Earth and environmental scientists trained in exascale computing techniques. We do this through a holistic and multidisciplinary program targeting excellence in both the technical (graphical processing unit, GPU) skills required for the development and application of software and multidisciplinary ‘domain’ training in the complexity of simulated Earth system processes.

Benefits of the Programme

• A fully funded PhD project (3.5 years) supported by a Research Training Support Grant;
• Research and professional skills training, outreach events, summer schools and workshops;
• Internship and placement opportunities;
• A supervisory team consisting of multidisciplinary supervisors;
• The choice of undertaking your PhD at any of our partner institutions;
• Research and training opportunities in state-of-the-art GPU programming and modelling techniques intended for exascale computing applications;
• Opportunities for PhD research topics across a diverse range of Earth and environmental science disciplines.

Funding and Eligibility

Funding covers tuition fees for UK home applicants, as well as an annual stipend at the standard UKRI rate (currently £19,237). To be classed as a home applicant, candidates must meet the following criteria:

• Be a UK national or UK/EU dual national or non-UK national with settled status / pre-settled status / indefinite leave to remain / indefinite leave to enter / discretionary leave / EU migrant worker in the UK or non-UK national with a claim for asylum or the family member of such a person, and
• Have ordinary residence in the UK, Channel Islands, Isle of Man or British Overseas Territory, at the Point of Application, and
• Have three years residency in the UK, Channel Islands, Isle of Man, British Overseas Territory or EEA before the relevant date of application unless residency outside of the UK/ EEA has been of a temporary nature only and of a period less than six years.

There are a limited number of international studentships available for exceptional candidates who do not meet the UK home studentship status eligibility requirements. Funding for successful international students will match that of home students and no international top-up fees will be payable.

Applicants must have, or expect to obtain, the equivalent of a 1st or 2:1 degree in a relevant subject, e.g., chemistry, computing science, earth/geo/environmental sciences, engineering, geography, mathematics etc.

Equality, Diversity and Inclusion

ExaGEO, and our partner institutions, champion EDI, believing that this is the way to increase research productivity and quality and to enhance societal and economic impact.

How to Apply

Applications must be submitted through the University of Glasgow’s online application system, regardless of where the PhD will be based. For full details of what is required, please visit our website here.

Contact Us

exageo-info@glasgow.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 January 2026.

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

Application deadline 1st December

Submissions details:

• Closing date:17:00, Monday 1 December 2025
• What to include:1 x Short CV (max 2 pages) and 1 x Statement explaining how the applicant addresses the criteria for the award (max 2 pages)
• (Optional):Provide any further details relevant to your application. This section is optional and can be up to 200 words. You should not use it to describe additional skills, experiences, or outputs, but you can use it to describe any factors that provide context for the rest of your application (for example, details of career breaks if you wish to disclose them).
• Submitting: Documents to be emailed to:dsi@lancaster.ac.uk

1. Early Career Researcher Award

This award recognises exceptional academic contributions to data science and AI by researchers who are in the early stages of their careers.

Eligibility:

• Researchers within 5 years of completing their PhD (or equivalent).
• Current members of DSI and DSI alumni who left LU <12 months before the closing date.
• Actively engaged in academic research in data science, AI, or related fields.

Assessment Criteria:

• Research Innovation: Demonstrated contributions to innovative, high-impact research in data science or AI.

• Mentorship and Collaboration: Active engagement with the data science and AI community (formal or informal), including advocacy that creates opportunities for researchers and support for underrepresented groups.

• Potential for Growth: Clear potential to make lasting, positive contributions to the academic community.

2. Diversity in Data Science Champion Award

This award celebrates academic researchers, faculty, or research teams who have made outstanding contributions to diversity, equity, and inclusion within data science and AI, with a focus on creating pathways for historically underrepresented and underserved groups.

Eligibility:

• Open to researchers, faculty, or teams.
• We are looking for individuals or teams that embed diversity, inclusion, and equity in their academic work and/or actively foster these values within their team and beyond through community engagement.

We note the potential diversity of contributions. Applications should address at least one of the following, as relevant to your specific contribution.

Indicative Assessment Criteria:

• Leadership in Inclusivity: Demonstrated efforts to create inclusive academic spaces for historically underrepresented or underserved groups in data science and AI.
• Institutional Change: Evidence of driving institutional or structural change that supports diversity, equity, and inclusion.
• Mentorship & Advocacy: Active involvement in mentorship or advocacy programs that support underrepresented or underserved students and early-career researchers in data science or AI.
• Community Engagement: Efforts to collaborate with or serve underrepresented communities within or outside of academia.

3. Excellence in Data Science & AI

This award recognises individuals who have demonstrated outstanding academic contributions to data science and AI, with priority given to those who have promoted inclusivity and supported underrepresented communities.

Eligibility:

• Open to members of DSI who are >5 years out of their PhD.

Indicative Assessment Criteria:

• Research Excellence & Impact: A record of impactful, high-quality research in data science or AI with evidenced contributions to the academic community and beyond.
• Educational Leadership: Contributions to developing inclusive educational practices, mentoring, and fostering diverse talent in data science and AI.
• Broader Community Influence: Recognition of the individual’s role in shaping the field in a way that includes and elevates underrepresented voices and perspectives.

Format: Hybrid Connect, Collaborate, Conquer: An CEEDS & DSI ECR Symposium

Schedule

09:30 - 09:45 Intro: CEEDS and DSI.

09:45 - 10:45 Talks

10:45 - 11:00 coffee break

11:00 - 11:30 research topic discussion/ or more talks

11:30 - 12:00 Group activity: feedback collection and ideas on activities that can benefit ECR, possible collaborations (grant opportunities) discussion, and actions.

12:00 - 13:00 Lunch and close: Announce future activities (invite CEEDS and DSI representatives)

Please sign up on Eventbrite, Environmental Data Science to be on the list for further information.

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

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