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Methods and tools for real-time coastal cliff monitoring using Internet of Things and Artificial Intelligence.


   Faculty of Science & Technology

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  Dr Marios Angelopoulos  No more applications being accepted  Funded PhD Project (Students Worldwide)

About the Project

Coastal landslides and cliff failures represent a significant hazard to vulnerable societies living and working in the coastal zone, where they engender rapid rates of shoreline retreat threatening the economies, health and well-being of communities. Financial losses from landslides are estimated to be considerably in excess of £10 million per year and have also caused the loss of human lives, such as the fatal incident at Burton Bradstock, Dorset, in 2012. Coastal defences, such as groynes and seawalls, offer some benefit but shift problems of heightened erosion downdrift due to disruptions in sediment supply. Currently there is a lack of understanding of the complex geological and hydrogeological factors that could operate over the medium to longer term, and how they affect wider shoreline management. Furthermore, currently established shoreline management plans rely on surveying and monitoring methods (such as drilling and instrumenting boreholes, LiDAR scans, GPS surveys) that are costly, difficult to deploy and require manual extraction of data resulting in significant lead times. Monitoring of coastal sites is timely and relevant, particularly in the context of the ongoing climate crisis and its increasing impact on coastal communities.

The aim of this studentship is to conduct research on, develop and validate novel data-driven methods and tools for coastal cliff monitoring employing Internet of Things (IoT) and Artificial Intelligence (AI). This interdisciplinary project will establish how IoT and AI technologies can be effectively incorporated in the study of the natural environment via the prism of monitoring coastal cliffs and landslides.

In particular, the selected PhD candidate will research and work on the following: 1) Developing a pioneering, cost-efficient sensing system for geological processes by conducting research on cutting edge IoT technologies of ultra-low-power electronics and Low-Powered Wide Area Networks (LPWANs); 2) Developing a cutting edge Data Management Platform employing highly novel cloud architectures and AI models for extracting new knowledge on geological processes using dense sensory data; 3) Validating the monitoring system in terms of its efficacy, cost-effectiveness and transferability to different settings in collaboration with the Dorset Council (DC) with support from the British Geological Survey (BGS).

The envisioned system will be trialled at Lyme Regis, Dorset; an area characterised as a Site of Special Scientific Interest (SSSI), and part of the Jurassic Coast World Heritage Site.

Applications are made via Bournemouth University’s website by clicking ’institution website’ button. If you have an enquiry about this project please contact us via the ’Email institution’ button, however your application will only be processed once you have submitted an application form as opposed to emailing your CV to us.

Candidates for a PhD Studentship should demonstrate outstanding qualities and be motivated to complete a PhD in 4 years and must demonstrate:

  • Outstanding academic potential as measured normally by either a 1st class honours degree (or equivalent Grade Point Average (GPA) or a Master’s degree with distinction or equivalent
  • An IELTS (Academic) score of 6.5 minimum (with a minimum 6.0 in each component, or equivalent) for candidates for whom English is not their first language and this must be evidenced at point of application.

Additional eligibility criteria:

  • BSc or MSc in Computing, Computer Engineering, Computer Science, Electrical and Electronics Engineering or other related fields. Applicants that already hold an MSc or are about to graduate will be given priority
  • Prior experience on Internet of Things systems, LPWAN technologies and Machine Learning for embedded systems is a plus
  • Prior experience on conducting academic research (e.g. co-authoring academic publications) is a plus
  • Prior experience with being involved in R&D projects is a plus.

Funding Notes

Funded candidates will receive a maintenance grant of £15,921 per year to contribute towards living expenses during the course of your research, as well as a fee waiver for 36 months.
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