or
Continue with FacebookLooking to list your PhD opportunities? Log in here.
Prof Neil Entwistle, Prof William Holderbaum ,
Dr Thomas O'Shea
Applications accepted all year round
Self-Funded PhD Students Only
Manchester
United Kingdom
Artificial Intelligence
Climate Science
Data Analysis
Data Science
Environmental Engineering
Environmental Geography
Geographical Information Systems Gis
Hydrology
Machine Learning
Physical Geography
About the Project
This project will:
· Address several pressing environmental challenges by using expertise in computational and environmental modelling, data analysis, Artificial Intelligence and Machine Learning.
· Emphasise engagement and co-evolution with stakeholders and end-users, with scope for large scale impact at the local, regional, national and international scales.
Initially, the focus will be on Nature Based Solutions (NBS) mapping at the catchment scale using data analysis skills that enable efficient scaling and scenario optimisation between multiple models and outputs to generate tangibly predictive outcomes pending further data input.
Modelling exercises will be developed toward demonstrating improved results or scenarios using NBS, ideally with scope for better understanding the potential future impacts of climate change to risk of assets (assets being anything from flood risk to buildings/industry to drought risk to lakes/reservoirs, etc).
The ability to digitise OS data and identify and quantify environmental change over time will be a necessary step for this so substantial experience with image processing software, including GIS (geographic information systems) (such as QGIS or ArcGIS) is imperative. A working knowledge of hydrological and hydrodynamic modelling software (TuFlow, HEC-RAS etc.) is required.
The utilisation of Machine learning or deep learning models (such as convolutional neural networks) to automate these analyses would be the anticipated next step for the successful candidate and so an interest in, and / or the ability to develop, this approach to bring together other system modelling elements using coding methods (e.g. Python) is desirable.
Familiarity with climate data interpretation and model integration would be desirable, particularly as the latter elements of this project will seek to develop integrated projections of change across the socio-environmental system interface.
A working familiarity with the related methods of data analyses, modelling and communication of results to audiences of different, cultures, backgrounds and levels of understanding will be essential.
The successful applicant will be based across SEE and ThinkLab at UOS.
Enquiries should be made with Prof. Enwistle (n.s.entwistle@salford.ac.uk) and / or Dr. O’Shea (T.E.OShea@salford.ac.uk)
References
O'Shea, T., Bates, P., and Neal, J.: Testing the impact of direct and indirect flood warnings on population behaviour using an agent-based model, Nat. Hazards Earth Syst. Sci. Discuss., published: August 2020.
O’Shea, T.E. & Lewin, J.: Urban flooding in Britain: An approach to comparing ancient and contemporary flood exposure, Nat. Haz., published: July 2020.
Lewin, J. & T.E. O’Shea: The shape-shifting form of UK floodplains: fusing analysis of the territorially constructed with analysis of natural terrain processes. PIPG, 2023.
Bates et al., A climate-conditioned catastrophe risk model for UK flooding. Nat. Hazards Earth Syst. Sci., 2023.
Jones et al., AI for climate impacts: applications in flood risk. npj climate and atmospheric science. 2023.
Zennaro et al., Exploring machine learning potential for climate change risk assessment. Earth-Science Reviews. 2021.
Richards et al., Harnessing generative artificial intelligence to support nature-based solutions. People & Nature. 2024.
O’Shea, T.E. & Lewin, J.: Urban flooding in Britain: An approach to comparing ancient and contemporary flood exposure, Nat. Haz., published: July 2020.
Lewin, J. & T.E. O’Shea: The shape-shifting form of UK floodplains: fusing analysis of the territorially constructed with analysis of natural terrain processes. PIPG, 2023.
Bates et al., A climate-conditioned catastrophe risk model for UK flooding. Nat. Hazards Earth Syst. Sci., 2023.
Jones et al., AI for climate impacts: applications in flood risk. npj climate and atmospheric science. 2023.
Zennaro et al., Exploring machine learning potential for climate change risk assessment. Earth-Science Reviews. 2021.
Richards et al., Harnessing generative artificial intelligence to support nature-based solutions. People & Nature. 2024.
Open Days
Register your interest for this project
The university will respond to you directly. You will have a FindAPhD account to view your sent enquiries and receive email alerts with new PhD opportunities and guidance to help you choose the right programme.
It looks like you alredy have a FindAPhD Account
Log in to save time sending your enquiry and view previously sent enquiries
* required field
The information you submit to University of Salford will only be used by them or their data partners to deal with your enquiry, according to their privacy notice. For more information on how we use and store your data, please read our privacy statement.
Search suggestions
Based on your current searches we recommend the following search filters.
Check out our other PhDs in Manchester, United Kingdom
Start a New search with our database of over 4,000 PhDs
PhD suggestions
Based on your current search criteria we thought you might be interested in these.
Artificial intelligence and machine learning methods for model discovery in the social sciences
University of Sheffield
Advancing Diagnostic Radiology through Artificial Intelligence and Machine Learning [SELF-FUNDED STUDENTS ONLY]
Cardiff University
Cyber Security, Artificial Intelligence, Machine Learning and Blockchain Technology: Mitigating Cyber Attacks and Detecting Malicious Activities in Network Traffic
University of Bradford
