Research Associate: Generative AI and LLM for 3D Geological Modelling (758830)

University of Strathclyde; Glasgow; Hybrid; GBP 37,000 - 47,000; Full time

Job summary

An innovative educational institution in Glasgow is seeking a Postdoctoral Research Associate to work on a fixed-term project combining AI and geoscience. You will develop generative AI models and integrate them with geological data to enhance underground infrastructure design. The position offers a salary range of £37,694 to £46,049 per annum and flexible hybrid working arrangements. A PhD in a relevant field is essential, along with experience in generative AI models.

Qualifications

• PhD (or near completion) in a relevant field.
• Experience with generative AI models or multimodal data fusion.
• Hands-on experience with fine-tuning open-source LLMs.

Responsibilities

• Develop generative AI models for 3D geological modeling.
• Fine-tune open source LLMs for geological understanding.
• Integrate LLM-based agents with geological models.

Skills

Machine learning foundations

Generative AI models (GANs, VAEs, diffusion models)

Fine-tuning open-source LLMs

Proficiency in Python

Education

PhD in Computer Science, AI, Machine Learning, Engineering, or related field

Tools

PyTorch

TensorFlow

Job description

FTE: 1 FTE (35 hours per week)

Term: Fixed (18 months)

We invite applications from talented and ambitious researchers for a Postdoctoral Research Associate position (fixed term, 18 months) in a pioneering project that brings together AI and geoscience. You will collaborate with an international, cross-disciplinary team from the University of Strathclyde, the Technical University of Denmark (DTU), and COWI - a world leader in engineering consultancy.

Tunnelling and underground infrastructure projects face significant challenges from unpredictable geological conditions, such as fault zones and variable rock masses, which can lead to delays, cost overruns, and safety risks. The project aims to tackle this challenge through Generative AI and Large Language Models (LLMs), developing intelligent systems that integrate diverse geological datasets into realistic 3D geological models and enable natural‑language exploration of geological risk. Your work will contribute to a cutting‑edge digital framework for safer, more efficient, and more sustainable underground infrastructure design.

Within a supportive and collaborative setting, the post holder will:

• Develop and apply generative AI models to combine varied geological data (e.g., borehole logs, maps) into multi‑realisation 3D geological models with quantified uncertainty.
• Fine tune open source LLMs to enhance geological domain understanding and terminology handling.
• Integrate LLM‑based agents with 3D geological models to enable natural language interaction, identification of geological features and risk zones, and automated reporting.
• Collaborate with domain experts to validate model outputs and refine methodologies.
• Disseminate results through high‑impact publications and presentations at international conferences.

You will have the freedom to shape technical directions within the project scope, with mentorship from the domain specialists.

We are seeking a highly motivated and research driven individual with strong foundations in machine learning.

Essential qualifications:

• A PhD (or near completion) in Computer Science, Artificial Intelligence, Machine Learning, Engineering, or a related field.
• Proven experience with generative AI models (GANs, VAEs, diffusion models) or multimodal data fusion.
• Hands‑on experience fine‑tuning open‑source LLMs and building LLM‑based agentic systems.
• Proficiency in Python and modern ML frameworks (e.g., PyTorch or TensorFlow).

Desirable experience:

• Experience with geoscience data or 3D geological modelling software (e.g., Leapfrog).
• Knowledge of uncertainty quantification approaches in AI.
• Practical experience using agentic AI frameworks (e.g., LangChain).
• Excellent communication skills suited for collaboration within an interdisciplinary research team.
• Strong track record of publishing research in high‑quality, peer‑reviewed journals or conferences.

This position is full time and available for a fixed term of 18 months. The post holder will be based in the Department of Civil and Environmental Engineering at the University of Strathclyde in Glasgow, UK. Flexible hybrid working arrangements are available, subject to agreement with the project leads. Candidates who have not yet been officially awarded their PhD will be appointed as a Research Assistant at the salary range of £33,002 - £36,636 per annum, depending on their current stage in their PhD program.

For informal enquiries, please contact Dr Stephen Suryasentana, [e-mail].

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£37,694 to £46,049 per annum