About this position
Can AI learn to reason like a human - and recognise when it isn’t sure? This PhD tackles that challenge in a high-impact setting: interpreting 3D digital models of rock formations built from drone and laser-scan data. These models underpin decisions impacting net-zero ambitions, yet interpretation remains slow and subjective.
We need tools that make geological reasoning faster, more transparent, and more consistent. Current AI systems struggle because they assume a single correct answer, when geological evidence often supports multiple plausible interpretations; so, training AI to copy one expert’s labels fails outside narrow cases.
The project investigates how to build AI to handle uncertainty - representing alternative interpretations, explaining the evidence behind them and supporting experts in making better-informed decisions.
You’ll gain expertise in 3D deep-learning, semantic technologies, explainable and uncertainty-aware AI, and human-centred evaluation, and test your work in VRGeoscience’s commercial platform VRGS.
Objectives
The objectives include:
- Build a benchmark dataset capturing how multiple experts interpret the same data, including their confidence in each judgement.
- Develop deep-learning and 3D computer-vision models that detect features while representing a distribution of plausible interpretations.
- Encode geological relationships in a knowledge graph that stores alternative readings of the same observations with evidence and provenance supporting each interpretation.
- Study human-AI collaboration and explainability through user studies.
Funding
These are doctoral teaching assistant positions that combine a PhD programme with a university teaching contract. Your time will be split approximately 60% on research and 40% on teaching. This provides excellent preparation for candidates considering an academic career at a university.
The teaching component will typically run over the 22 teaching weeks per year and the 4 assessment weeks. You will help deliver an outstanding student experience by supporting lead academics with classroom and lab teaching and assessment, further building the skills developed within your PhD research programme.
The position is grade 6 with a current salary of £31,236 and includes payment of home PhD tuition fees for the duration of the 6-year award. Home students can apply. Applicants must have the right to work in the UK. We are unable to offer visa sponsorship for this role.
Candidate requirements
Applicants should hold (or expect) a first or upper-second class UK honours degree, and ideally a masters degree, in computer science, data science, artificial intelligence, software engineering, geoinformatics, or a strongly quantitative geoscience discipline.
Essential
- Experience of game development using engines such as Unity, evidenced through a portfolio of work submitted with their application.
- Strong programming skills and the ability to undertake independent research to a high academic standard.
How to apply
If you have any questions, contact the principal supervisor, Dr Rochelle Taylor .
To apply you will need to complete the online application form for a part time PhD in Computing and Digital Technology.
Please complete the Doctoral Project Applicant Form , and include your CV and a covering letter to demonstrate how your skills and experience map to the aims and objectives of the project, the area of research and why you see this area as being of importance and interest.
Please upload these documents in the supporting documents section of the University’s Admissions Portal or send them to the PGR Admissions team at pgradmissions@mmu.ac.uk .
Quote reference: SciEng-DTA Jan 2027-RT-Semantic Outcrop Reasoning.
Funding
GBP 31,236 per year
How to apply
- Read the full advert on the source site — it carries the authoritative terms.
- Email the contact below with your CV and a short, specific message. See Emailing Professors.
- Prepare your SOP, CV, transcripts and referees before the deadline.
- Apply through the university's own portal. Never pay a fee to a third party.