£17.33 per hour plus £2.09 holiday pay
Advertising End Date
17 Apr 2026

Role & Department Overview

This role is part of an Impact Acceleration Project (IAA) in partnership with Equans UK, a provider of solar farms globally. This project is exploring data-driven ways to perform predictive maintenance on solar assets, and to identify and quantify their reliability. They are also interested in data-driven understanding of how repairable an asset is, as this will feed into their procurement decisions and ensure they foster a 'culture of repair' as part of their sustainability strategy.
 
You will join a team split across the School of Computing and Communications and Engineering departments. This is a fantastic opportunity for you to further develop your data science capabilities, whilst working on a project that will improve the financial viability and sustainability of solar energy. 
 
During the project you will work towards answering some of the following questions:
  • Can faults and other failures in solar assets be identified in advance through data analysis?
  • Which data features are the best predictors of solar asset failure?
  • How can we measure the resilience of solar energy assets?
  • How can we use data-driven analysis to identify how 'repairable' an asset is?
Answering this may require you to use existing 'off the shelf' mathematical/statistical/machine learning/AI techniques, or develop your own. The team have significant experience in this area, and we will support you to do this. You will also develop demonstrators to showcase your techniques in action, with a goal being to provide Equans with tools and techniques they can implement.
 
In addition to having a beneficial impact on Equans' operations, the team are keen to produce publishable research outputs. You will have the opportunity to feed into, or lead, on this and you the opportunity to present your work at suitable events.
 

Pay Rate: £17.33 per hour plus £2.09 holiday pay

Start Date: 1st May 2026

End Date: 31st July 2026

Job Description

The candidate employed on this role is expected to:

  • Meet with supervisors, industrial partners and other key stakeholders on a regular basis to discuss project management and technical matters
  • Perform data cleaning and exploratory analysis of datasets relating to solar panel operations.
  • Analyse datasets relating to solar panel operations using a range of data-driven techniques, for example anomaly detection or predictive monitoring.
  • Present data analysis and findings back to project stakeholders.
  • Develop conceptual demonstrators of the analysis and monitoring techniques for tools associated with project stakeholders.
  • Contribute to reports highlighting project findings.

Person Specification

The candidate employed on this role is expected to:

  • Have, or be working towards, qualifications in subjects that have a strong numerical component. Ideally these would contain some data science content.
  • Have knowledge of common data science techniques, and a desire to learn new techniques as appropriate.
  • Meet deadlines associated with the milestones of the project
  • Be able to produce reports, presentations and equivalent documentation to a standard appropriate for distribution with shareholders

Working in this role will help develop the following skills and experience:

  • Collaboration
  • Analysing
  • Commercial Awareness
  • Planning and Organising
  • Exercising Professional Judgement
  • Problem Solving

You are required to submit a cover letter to support your application. Applications without a cover letter will not be considered.

Please note: Unless specified otherwise in the advert wording, this role is only open to individuals living in the UK.

Under the terms of this work, we endeavour to provide the advertised number of hours however, hours are not guaranteed and that work may cease if there is a fall in demand. 

Adverts that display a closing date should be treated as a guide. We reserve the right to close the vacancy once we have received sufficient applications, so we advise you to submit your application as early as possible to prevent disappointment.

Apply

Help and advice on making applications can be found on the Lancaster University Careers pages. Visit www.lancaster.ac.uk/careers.

Copyright © 2026 Powered by Eploy
Cookie Policy