Research Associate, Digital Twins for Energy Efficient and Healthy Built Environments
The Alan Turing Institute is the UK’s national institute for data science and artificial intelligence whose mission is to make great leaps in data science and artificial intelligence research in order to change the world for the better.
We are seeking to recruit a postdoctoral research associate to work in the area of uncertainty quantification and inference from energy models of buildings, specifically to support the decarbonisation of the building stock.
This project builds on ongoing research within the Data-Centric Engineering programme which has been pioneering: (i) the development of Bayesian calibration strategies for large-scale simulation models of built environments under sparse data (ii) methods for inference and updating of time-varying parameters in energy models (iii) exploitation of new and diverse forms of data to develop data-centric energy models of occupancy. This position is for researching new forms of integrating data and simulation models in support of the decarbonisation potential of buildings at the city scale, whilst at the same time improving the urban environment in terms of health and liveability.
This post is an appointment to the Digital Twins of Built Environment group in the Data-centric Engineering Program at Turing. You will be supervised by Dr. Ruchi Choudhary (Cambridge, Engineering) along with other academic collaborators (see www.eeci.cam.ac.uk).
Duties & Responsibilities
- To pursue individual and collaborative research of outstanding quality, consistent with making a full active research contribution in line with the research strategy outlined by the PIs.
- To write or contribute to publications or disseminate research findings using other appropriate media.
- To attend and present research findings and papers at academic and professional conferences, and to contribute to the external visibility of the Institute.
- To ensure compliance with secure handling of data and health and safety in all aspects of work.
- To participate in and develop internal and external partnerships, for example to identify sources of funding, obtain projects, or build relationships for future activities.
- PhD in Engineering, or an equivalent advanced qualification in Building Physics, Building Simulation, and/or finite element models of building energy.
- Expertise in application and implementation of Bayesian Inference or Probabilistic Machine Learning applied to the field of Building Performance (or a closely related discipline).
- Demonstrated ability to initiate, develop and deliver high quality research aligned with the research strategy indicated by the PI.
Please see our portal for a full breakdown of the Job Description.
Terms and Conditions
This post is an immediate start and is offered on a fixed term basis until 28 February 2023, at an annual salary of £35,000-£41,000 plus excellent benefits, including flexible working and family friendly policies, https://www.turing.ac.uk/work-turing/why-work-turing/employee-benefits
Please see our jobs portal for full details on how to apply and the interview process.
Equality Diversity and Inclusion
The Alan Turing Institute is committed to creating an environment where diversity is valued and everyone is treated fairly. In accordance with the Equality Act, we welcome applications from anyone who meets the specific criteria of the post regardless of age, disability, ethnicity, gender reassignment, marital or civil partnership status, pregnancy and maternity, religion or belief, sex and sexual orientation. Reasonable adjustments to the interview process can also be made for any candidates with a disability.