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Research Associate in Net Zero Future Heating System Design and Evaluation

University of Bath - Architecture & Civil Engineering

Location: Bath
Salary: £37,099 to £44,263 Grade 7
Hours: Full Time
Contract Type: Fixed-Term/Contract
Placed On: 29th July 2024
Closes: 1st September 2024
Job Ref: FM11937

About the role

Applications are invited for a highly motivated and ambitious Research Associate to work as a member of a multi-disciplinary team in the Department of Architecture and Civil Engineering.

Buildings have increasing potential to be active participants in the transition to Net Zero. Demand side energy management, energy storage, and even the thermal mass of a building structure, have potential to facilitate and enhance energy flexibility. The ability to shift the timing of building energy demands and the decoupling of heating energy supply from demand are important levers in optimisation of buildings and energy network interaction, including the utilisation of renewable energy resources. However, retaining the high fidelity associated with building level energy modelling is challenging when scaled-up and aggregated to street, community, or higher levels.

This project will seek to develop computational modelling of building energy use with improved fidelity at scale. The aim of this modelling is to deliver accurate insight into the impact of future heating technologies and strategy options for residential buildings.

This is a full-time position with an expected end date of 1st September 2026.

For an informal discussion about each role please contact Dr Andy Shea on a.shea@bath.ac.uk. Please ensure you submit your application via our website.

About you

Your skills and attributes: 

  • a PhD in an engineering/physical sciences subject
  • demonstrable skills in energy modelling including programmatic use of EnergyPlus, or similar building energy modelling tools
  • experience in delivering research project results in a built environment context, i.e. a record of peer-reviewed journal and conference papers in this or a closely related area

Experience in AI/ML tools for advanced building energy modelling is desirable.

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