Developing techniques to deal with large environmental data sets

Director of Studies: Dr Craig McNeile
2nd Supervisor: Dr Luciana Dalla Valle 

Applications are invited for a 3.5 years PhD studentship within the Environmental Intelligence doctoral training programme at the University of Plymouth. The studentship will start on 01 October 2024. 

There are now much larger and varied data sets about the environment from, for example: satellite images, results from the internet of things, and even social media posts. In order to monitor the environment and to develop strategies that lead to Net Zero requires that the relevant data is combined together to form big data sets that can be used in machine learning. Large data sets are stored in a variety of different databases, including traditional relational databases and more modern NoSQL databases. To organize the data, the FAIR (Findable, Accessible, Interoperable, Reusable) principles will be used. To process large data sets requires High Performance Clusters (HPC). The University of Plymouth is installing a new HPC system in 2024 that the student will be able to use this system for machine learning with large environmental datasets. 

The student will investigate data engineering techniques, such as ETL, to combine and clean large data sets on environmental data into a datalake at the University of Plymouth. The datalake will be available to researchers at the University of Plymouth and the PhD student will use the extracted data for machine learning using the High Performance Computing cluster at the University of Plymouth. The performance of handling large data sets will be studied.

Training on environmental science will be provided by the student attending relevant MSc modules. The PhD will learn how to use the High Performance Cluster by working with a member of the HPC team and attending training courses provided by the national computer centres and vendors such as Intel. The University of Plymouth is a member of the Turing network, so the student will attend training events from them.

The candidate should have some experience with working with databases and an interest in applying machine learning to analyse large environmental datasets. Ideally the candidate should have a MSc in data science, or computing, or other STEM discipline.

Ravat, Franck and Zhao, Yan Data Lakes: Trends and Perspectives. (2019) In: International Conference on Database and Expert Systems Applications (DEXA 2019), 26 August 2019–29 August 2019 (Linz, Austria). https://oatao.univ-toulouse.fr/25043/1/ravat_25043.pdf. 

Applicants should have a first or upper second class honours degree in computing or data science or a relevant masters qualification. 

If your first language is not English, you will need to meet the minimum English requirements for the programme, IELTS Academic score of 6.5 (with no less than 5.5 in each component test area) or equivalent. 

The studentship is supported for 3.5 years and includes full home tuition fees plus a stipend of £19,088 2024/25 rate (TBC). The studentship will only fully fund those applicants who are eligible for home fees with relevant qualifications. Applicants normally required to cover international fees will have to cover the difference between the home and the international tuition fee rates approximately £12,697 per annum 2023/24 rate (2024/25 rate TBC).

NB: The studentship is supported for 3.5 years of the four-year registration period. The subsequent 6 months of registration is a self-funded ‘writing-up’ period.

If you wish to discuss this project further informally, please contact Dr Craig McNeile.

Please see our how to apply for a research degree page for a list of supporting documents to upload with your application.

For more information on the admissions process generally, please visit our how to apply for a research degree webpage or contact The Doctoral College at research.degree.admissions@plymouth.ac.uk.

Shortlisted candidates will be invited for interview after the deadline. We regret that we may not be able to respond to all applications.  Applicants who have not received a response within six weeks of the closing date should consider their application has been unsuccessful on this occasion.