PDRA for ENI UK Energy Futures Project at University of Manchester

PDRA for ENI UK Energy Futures Project at University of Manchester

Manchester Full-Time 30000 - 40000 £ / year (est.) No working from home possible
The University of Manchester

At a Glance

  • Tasks: Develop thermodynamic simulations for CO₂ geothermal systems and collaborate with industry partners.
  • Company: Join the University of Manchester, a leader in energy transition research.
  • Benefits: Enjoy 29 days annual leave, generous pension contributions, and professional development opportunities.
  • Other info: This is a fixed-term Grade 6 role starting July/August 2026.
  • Why this job: Contribute to innovative low-carbon technology research within a dynamic team environment.
  • Qualifications: PhD in engineering with expertise in fluid flow modelling and advanced computational methods required.

The predicted salary is between 30000 - 40000 £ per year.

We are seeking a motivated and collaborative individual to join our team as a Postdoctoral Research Associate. This role offers an exciting opportunity to contribute to cutting‑edge research supporting the UK’s energy transition, as part of the UK Energy Futures Research Partnership, a major collaboration between leading UK universities and industry. The post will focus on advancing research into CO₂ Plume Geothermal (CPG) systems, an emerging low‑carbon technology that combines geothermal energy production with permanent CO₂ storage. You will work within a dynamic and inclusive research environment, collaborating closely with academic colleagues and industry partners, including Eni S.p.A., to develop innovative modelling approaches for sustainable geoenergy systems. This is a fixed‑term Grade 6 role, starting July/August 2026, based at the University of Manchester. We welcome candidates who bring diverse perspectives, experiences, and approaches to their work.

Responsibilities

  • Develop advanced thermodynamic and compositional reservoir simulations for CO₂‑based geothermal systems
  • Investigate the impact of geological heterogeneity on CO₂ flow, heat extraction and long‑term storage
  • Apply GPU‑based modelling frameworks and machine‑learning‑enhanced approaches to fluid flow in porous media
  • Collaborate with academic and industry partners across the UK Energy Futures consortium
  • Disseminate research through high‑quality peer‑reviewed publications, reports and conference presentations

Essential Criteria

  • A PhD in a relevant engineering discipline (e.g. chemical engineering, fluid mechanics or reservoir engineering)
  • Proven research expertise in fluid flow modelling in porous media
  • Experience with advanced computational approaches, including GPU‑based modelling and/or data‑driven methods
  • A strong publication record relevant to the project area
  • Ability to work collaboratively within multidisciplinary and international research teams

Desirable Criteria

  • Expertise in fluid flow modelling across multiple length scales
  • Experience contributing to supervision of postgraduate research projects
  • Experience developing or working with in‑house or industry‑standard simulation tools

Benefits

  • Generous employer pension contribution
  • 29 days annual leave plus bank holidays, along with Christmas closure
  • Access to extensive professional development and industry collaboration opportunities

Applications close at midnight on the closing date.

PDRA for ENI UK Energy Futures Project at University of Manchester employer: The University of Manchester

The University of Manchester offers a collaborative research environment focused on the UK’s energy transition. Employees benefit from 29 days of annual leave and a generous pension scheme. The team works closely with industry leaders like Eni S.p.A. on cutting-edge projects.

The University of Manchester

Contact Details:

The University of Manchester Recruitment Team

We think you need these skills to ace PDRA for ENI UK Energy Futures Project at University of Manchester

Thermodynamic Modelling
Reservoir Simulations
Fluid Flow Modelling
Geological Heterogeneity Analysis
GPU-based Modelling
Machine Learning Approaches
Data-driven Methods