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Micro-climate modelling for efficient urban green space design

   Vice Chancellor's PhD Studentships

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  Dr L Babu Saheer, Dr Mahdi Maktabdar Oghaz, Dr A Greig  No more applications being accepted  Funded PhD Project (UK Students Only)

About the Project

This transdisciplinary project will address the problems of providing clean air for urban regions through effective green space design and sustainable human behavioural modelling. It integrates contributions of several disciplines such as computing (Artificial Intelligence), urban design and global sustainability. The current surge of data generated by numerous Internet of Things (IoT) sensors and devices can be effectively used for developing creative urban planning and design solutions which addresses climate change and liveability in cities. The main aim of this project is to use data science and machine learning techniques to gather evidence, generate and understand the models for air quality and its relation to vegetation and social behavioural patterns. The outcome helps us propose planning strategies for a sustainable urban space to keep the carbon footprint under control. 

Complex mathematical models are available for traffic, energy, air quality, health and wellbeing, vegetation and nature conservation, however these are not easily scalable to a new region/data. Recently, researchers have been trying to develop Artificial Intelligence (AI) or specifically Machine Learning (ML) models to replace these mathematical models and capitalise on the power of technology and big data.  

Our previous research has proposed efficient and cost-effective framework for air quality modelling. The successful candidate will utilise a holistic approach of data collection using existing custom devices as an IoT network and gathering datasets on road traffic, vegetation and weather around cities(Cambridge/Colchester) to model a sustainable predictive AI system. Performance will be evaluated through held out test sets, followed by live deployment and testing in collaboration with the city authorities. The candidate will:

  • Collect air quality data, using this to develop and enhance micro-climate data models and policy generation frameworks to help design urban green spaces. 
  • Analyse behavioural patterns of urban dwellers and incorporate findings into the model as mode of transportation and traffic patterns. 
  • Test the effectiveness of the framework.

The successful candidate will be supervised by an experienced team with experience relevant to the topic. Dr Babu-Saheer is an expert in “AI for sustainability” and has successfully analysed pollutant concentration data for London/Cambridge (Saheer, 2020) and together with Dr Mahdi Maktabdar (expert in AI, IoT and Computer Vision) developed deep learning techniques to recognize number/species of trees from satellite imagery (Saheer, 2021). Dr.Greig is an expert with experience in large European urban Air quality project and mathematical modelling. She is the director of education for sustainability with substantial experience in supervising students in transdisciplinary topics.

 If you would like to discuss this research project please contact Dr Lakshmi Babu Saheer

Candidate requirements

Applications are invited from UK fee status only. Applicants should have (or expect to achieve) a minimum upper second class undergraduate degree (or equivalent) in a cognate discipline. A Masters degree in a relevant subject is desirable.

Applicants must be prepared to study on a full-time basis, attending at Cambridge campus. The Vice Chancellor’s PhD scholarship awards are open to Home fee status applicants only.

Application Procedures

Applications for a Vice Chancellor’s PhD Scholarship are made through the application portal on our website: https://aru.ac.uk/research/postgraduate-research/phd-studentships

We will review all applications after the submission deadline of 27th February. We will contact shortlisted applicants in the week commencing 14th March. Interviews will be held between 21st March – 1st April. The interview date for this project can be found on our website.

If you have any queries relating to the application process or the terms and conditions of the scholarships, please email vcphdscholarships(@)aru.ac.uk.

Documentation required

You will need the following documents available electronically to upload them to the application portal (we can accept files in pdf, jpeg or Word format):

  1. Certificates and transcripts from your Bachelor and Masters degrees, (if applicable)
  2. Your personal statement explaining your suitability for the project
  3. Passport and visa (if applicable)
  4. Curriculum Vitae

Funding Notes

This successful applicant for this project will receive a Vice Chancellor’s scholarship awards which covers Home tuition fees and provides a UKRI equivalent minimum annual stipend for three years. The award is subject to the successful candidate meeting the scholarship Terms and conditions which can be found on our website: https://aru.ac.uk/research/postgraduate-research/phd-studentships


Lakshmi Babu Saheer, Ajay Bhasy, Mahdi Maktabdar, and Javad Zarrin, Data Driven Framework for Understanding and Predicting Air Quality in Urban Areas, Submitted: Frontiers in Big Data 2022 [Under Review].
Babu Saheer, L. and Shahawy, M., Self-supervised approach for urban tree recognition on aerial images. In Artificial Intelligence Applications and Innovations. AIAI 2021 IFIP WG 12.5 International Workshops, eds. I. Maglogiannis, J. Macintyre, and L. Iliadis (Cham: Springer International Publishing), 476–486, 2021.
Waters, E., Maktabdar Oghaz, M., and Babu Saheer, L., Urban tree species classification using aerial imagery. In: International Conference on Machine Learning 2021, Workshop Tackling Climate Change with Machine Learning, 2021.
Lakshmi Babu Saheer, Mohamed Shahawy, Javad Zarrin, 2020. Mining and Analysis of Air Quality Data to Aid Climate Change. In Artificial Intelligence Applications and Innovations. AIAI 2020 IFIP WG 12.5 International Workshops. AIAI 2020. IFIP Advances in Information and Communication Technology, vol 585. Springer, Cham. https://doi.org/10.1007/978-3-030-49190-1_21
Rolnick, David, et.al., 2019. Tackling Climate change with Machine Learning, https://arxiv.org/pdf/1906.05433.pdf [Accessed on 25 November 2020]
Abdullah N. Al-Dabbous and Prashant Kumar, 2014. The influence of roadside vegetation barriers on airborne nanoparticles and pedestrians exposure under varying wind conditions. Atmospheric Environment, 90:113–124.
Ioannis Tsapakis and William H Schneider, 2015. Use of support vector machines to assign short-term counts to seasonal adjustment factor groups, Transportation Research Record: Journal of the Transportation Research Board, (2527):8–17.
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