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Research Fellow in Computational Modelling of Energy Geotechnics

Job in Guildford, Surrey County, GU1, England, UK
Listing for: UNIVERSITY OF SURREY
Full Time position
Listed on 2026-08-21
Job specializations:
  • Engineering
    Research Scientist, Mechanical Engineer, Environmental Engineer, Energy Engineer
  • Research/Development
    Research Scientist
Salary/Wage Range or Industry Benchmark: 35000 - 52000 GBP Yearly GBP 35000.00 52000.00 YEAR
Job Description & How to Apply Below

Organisation/Company UNIVERSITY OF SURREY Research Field Engineering Engineering Engineering Researcher Profile Recognised Researcher (R2) First Stage Researcher (R1) Final date to receive applications 7 Sep 2026 - 00:00 (UTC) Country United Kingdom Type of Contract Other Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure?

No

Offer Description

The University of Surrey is a global community of ideas and people, dedicated to life-changing education and research.

We are ambitious and have a bold vision of what we want to achieve - shaping ourselves into one of the best universities in the world, which we are achieving through the talents and endeavour of every employee.

Our culture empowers people to achieve this aim and to collectively, and individually, make a real difference.

The role

We are looking for an enthusiastic and highly motivated researcher to join an EPSRC-funded UKRI-Norway project on
Energy Micropile Retrofits for Climate Change Adaptation of Existing Buildings
. This is an exciting opportunity to contribute to research at the interface of
computational geotechnics, data-centric methods, sustainable infrastructure, low-carbon energy systems, and climate-change adaptation
.

The project aims to develop and validate energy micropile retrofit solutions that can support the decarbonisation of existing buildings by combining structural foundation performance with renewable thermal energy exchange. Based within the University of Surrey's School of Engineering, the successful candidate will lead the project's computational modelling strand, developing complex numerical models and validating them against datasets generated by collaborators. The candidate will use surrogate modelling and data-centric approaches to complement simulations, incorporating uncertainty to evaluate system resilience, and developing useful tools that will enable industry to adopt energy micropiles as a dual structural–energy retrofit solution.

This research will be performed in close collaboration with colleagues at the University of Surrey and with project partners at the Universities of Leeds, Cambridge and NTNU, as well as relevant industrial collaborators. The role offers the opportunity to undertake cutting-edge computational research, contribute to high-impact publications, and engage with a multidisciplinary consortium addressing an important challenge in sustainable infrastructure.

In this role, the successful candidate will develop and validate advanced numerical models of energy micropiles, undertake large-scale simulations and data-driven analysis to explore performance and optimise designs, assess the impact of uncertainty on retrofit outcomes, and translate research findings into practical tools and guidance for industry. The role also involves contributing to publications, conferences, technical reports and collaborative project activities, and would suit a researcher with strong computational, numerical modelling and programming skills who is motivated by real-world climate and energy challenges.

About you The successful candidate will have a
PhD, or be near completion of a PhD, in Geotechnical Engineering, Civil Engineering, Mechanical Engineering, Geo-energy, Energy Systems, Computational Mechanics or a related discipline
. You will have experience with numerical modelling and/or data-centric methods and a keen interest in applying these to energy and infrastructure challenges.

You should be able to demonstrate experience in numerical modelling of coupled thermal, hydraulic and/or mechanical processes, alongside strong scientific programming, data-handling and analytical skills. Experience in probabilistic analysis and uncertainty quantification within an engineering context, a track record of high-quality peer-reviewed research, and the ability to work independently and collaboratively are also essential, together with strong communication and problem-solving skills and a willingness to travel occasionally within the UK and internationally.

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