Project Title: Machine learning to determine soil properties: a smartphone-based solution for stakeholders
PhD Studentship: This 3.5 years fully funded doctoral program is a collaboration between Abertay University and the James Hutton Institute. Abertay University is one of the fastest growing universities in the UK. In the latest Guardian University League Tables, Abertay University is ranked 8th in the UK in Civil Engineering among 60 institutes and 1st in Scotland. Abertay University has been named UK University of Year for Teaching Quality by The Times & Sunday Times Good University Guide 2021. The James Hutton Institute (based in Dundee and Aberdeen) is one of the largest environmental and agricultural research institutes in the UK, carrying out research in climate change, soil science, crop breeding, agronomy and many other topics.
Project Description: Soil properties, such as organic carbon, can be estimated using modern data mining and modelling approaches. The proposed approach will correlate soil colour with soil structure metrics extracted from images produced by smartphone and tablet cameras, as well as environmental factors. This approach was used as a foundation to develop a mobile phone app which measures Scottish soil organic carbon content. This research project aims to build on work done to date to develop a new machine learning model that enhances existing performance and functionality. Furthermore, the project proposes analysing the pH, bulk density, particle size distribution and permeability of the soil using images. Following development of the model, a new SolEst app will be developed and tested, which provides an environmentally-friendly and costless platform for land managers working in the construction and agriculture sectors to analyse these and other soil properties using their mobile phone. This interdisciplinary project merges geotechnical engineering, digital technology and artificial intelligence to introduce a cutting-edge method for soil analysis using widely available smartphone technology.
The project requires a combination of modelling, laboratory and experimental work. The successful PhD candidate will be able to develop and/ or enhance skills in machine learning, programming, working in-situ and in laboratory during the course of the program.
Candidate specification:
Essential:
- Knowledge/experience in developing Machine Learning, Artificial Neural Networks
- Knowledge/experience in working with modelling software (e.g. Matlab, Java)
- UK/International driving licence
Desirable:
- Knowledge/experience in mobile phone app development
- Knowledge/experience in analysing soil physical and mechanical properties both in-situ and in the laboratory
- Track record of publication in related fields
Entry requirements: A First Class or high 2:1 Degree in Engineering, Computer Science or cognate discipline. Related MSc is desirable. Strong programming skills essential. Experience or interest in geotechnical engineering and engineering geology are desirable. Experience or interest in Machine Learning, Neural Networks, Genetic Programming or Optimisation are essential..
Funding: The studentship covers full national and international PhD tuition fees for 3.5 years and a tax-free stipend. International Students are welcome to applying and the project covers full international stipend and registration.
Applicants who are non-native speakers of English, the University requires IELTS of 6.5 (with no band less than 6.5) or an equivalent qualification accepted by the Home Office.
The Studentship is available for a June (preferred) or October 2021 start for a period up to 3.5 years.
Applicants should submit through the Abertay University jobs page https://www.abertay.ac.uk/about/working-at-abertay/jobs/, direct link: https://ce0173li.webitrent.com/ce0173li_webrecruitment/wrd/run/ETREC107GF.open?VACANCY_ID=5874063DXB&WVID=71870904bp&LANG=USA submitting a cover letter detailing why you are interested in undertaking this project, and a CV.
Deadline: 8th March 2021, 17:00.
Further details on this project can be obtained from the project principal supervisor, Dr Ehsan Jorat (e.jorat@abertay.ac.uk).