PhD studentship: Predicting coastal changes in space and time using geospatial data science
About the Project
This fully funded PhD studentship starting from October 2021 provides an excellent opportunity to take a leading role in the rapidly expanding area of environmental data science.
The PhD is in an exciting interdisciplinary project, developing cutting-edge data science approaches for predicting coastal change. Geospatial data from satellites, airborne and terrestrial sensors and from the Citizen Science observatory will be combined and interpreted through data fusion techniques. The project will develop novel deep learning and non-stationary wavelet techniques to turn the data into predictions of coastal change in near real-time. The outcomes will transform future practice in the monitoring and prediction of erosional hotspots, and inform operational and strategic decision making.
The project offers the exciting opportunity to work with world-class researchers from the Lancaster Data Science Institute and their partners, allowing the student to develop and deploy advanced data science techniques and develop the interdisciplinary skills needed to address environmental grand challenges.
Due to the interdisciplinary nature of this work, applicants with a range of experience who can demonstrate a strong interest in this topic will be considered. The project is ideal for a person who is interested in receiving advanced training in statistics/machine learning and its application to the environment. The person will ideally have an undergraduate or Masterís degree in statistics, data science or physics, or an environmental science qualification with a strong modelling component. Prior experience with computing software (one of e.g., Python, R, Matlab) is desirable. Students from EU or overseas are encouraged to discuss funding eligibility with supervisors.
To apply, please send your CV and a cover letter detailing your interest and experience to Dr Suzana Ilic (firstname.lastname@example.org) by 5pm on Monday 26th April 2021.
If you would like to discuss this project and/or funding eligibility, please reach out to any of the project supervisors: Dr Suzana Ilic (email@example.com), Dr Ce Zhang (firstname.lastname@example.org) or Dr Rebecca Killick (email@example.com). Applications are considered on a rolling basis.
Zhang, X., Su, H., Zhang, C., Gu, X., Tan, X., & Atkinson, P. Robust unsupervised small area change detection from SAR imagery using deep learning. ISPRS Journal of Photogrammetry and Remote Sensing, 173, 79-94 (2021).
Ward, N.D., Megonigal, J.P., Bond-Lamberty, B. et al. Representing the function and sensitivity of coastal interfaces in Earth system models. Nature Communication 11, 2458 (2020).
Bird, C.O., Sinclair, A.J., Bell, P.S., Green, C. Autonomous monitoring of nearshore geomorphology and hydrodynamics to assist decision making in coastal management, using shore-based radar systems: A case study on the Fylde peninsula, UK. In: ICE Coastal Management 2019, La Rochelle, France, 24 - 26 September 2019. Miles, A., Ilic, S., Whyatt, J. D., James, M. R. Characterizing beach intertidal bar systems using multi-annual LiDAR data. Earth Surface Processes and Landforms, 44(8), 1572-1583 (2019).
Bird, C.O., Bell, P.S., Plater, A.J. Application of marine radar to monitoring seasonal and event-based changes in intertidal morphology. Geomorphology, 285, 1-15 (2017).