|Funding for:||UK Students, EU Students, International Students|
|Funding amount:||Fully-funded (fees and maintenance)|
|Placed On:||3rd January 2023|
|Closes:||15th March 2023|
By 2035, UK's building stock is expected to deliver 63% reduction in total carbon emissions from 2019 levels. These reductions will require that new buildings conform to and maintain the highest energy efficiency standards, whilst also providing a healthy living environment that is resilient to future climate change.
The principal objective is to develop and test reproducible models for design and operation of multi-unit residential buildings with respect to operational energy use and occupant health. A second objective is to quantify and attribute key variables influencing the so-called performance gap in recently built buildings with respect to human comfort and energy use. The project will harness data from existing set of buildings and calibrate design stage numerical models to understand the performance gap and translate these into improved models for future buildings.
The project will investigate techniques for 'learning from data' and adapting energy models of recent multi-unit residential buildings in real-time for efficient use of energy and to maximise occupant health. These models will be transferrable to predict the behaviour of future buildings under climate change scenarios. The models will be designed to quantify uncertainties in predictions and translate these into risks of not meeting design specifications for fabric efficiency, overheating, energy, and water usage.
The models will be tested in terms of their computational tractability, reproducibility, and usability. The project will also investigate new effective forms of communicating stochastic outputs in forms that can be understood by decision-makers.
Under the broad framework of Digital twins, the thesis will thus develop new integrated energy tracking and modelling framework to improve resident behaviours relating to overheating, energy and water use in existing buildings and for the design of future buildings.
Applicants must demonstrate experience and good understanding of building physics and energy demand modelling. Good programming skills and experience in using Machine learning will be deemed advantageous.
Informal inquiries may be directed to Prof. Ruchi Choudhary (firstname.lastname@example.org)
EPSRC DTP studentships are fully-funded (fees and maintenance) for eligible UK students. EU and international students may be considered for a small number of awards at the UK rate. Full eligibility criteria can be found via the following link; https://www.postgraduate.study.cam.ac.uk/finance/fees/what-my-fee-status
Applicants should have (or expect to obtain by the start date) at least a good 2.1 degree in an Engineering or related subject.
Applications should be submitted via the University of Cambridge Applicant Portal www.graduate.study.cam.ac.uk/courses/directory/egegpdpeg, with Prof. Ruchi Choudhary identified as the potential supervisor.
The University actively supports equality, diversity and inclusion and encourages applications from all sections of society.
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