Heriot-Watt University, under the supervision of Assistant Professor Nanzhe Wang, is recruiting PhD students for research in AI-enabled geoenergy and subsurface systems. The research focuses on integrating artificial intelligence, physics-based modelling, scientific machine learning, and probabilistic algorithms to address challenges in subsurface energy systems. Applications include geological carbon storage, geothermal energy, subsurface hydrocarbon reservoirs, and groundwater systems, all supporting the energy transition and net-zero goals.

Key research topics include hybrid physics–AI modelling and inversion for geoenergy and subsurface flow systems, multi-source and multi-scale data assimilation, uncertainty quantification, digital twins for subsurface energy systems using generative AI and surrogate models, uncertainty-aware decision-making and control, and active learning for efficient inversion and optimization. The research is highly interdisciplinary, combining computer science, earth science, and environmental science.

Applicants should have a strong background in engineering, data science, geoscience, applied mathematics, physics, or computer science, with programming skills in Python, MATLAB, or PyTorch/TensorFlow. Experience or interest in subsurface flow modelling, geoenergy, and AI/machine learning is desirable. Candidates must be self-funded or have external funding, with possible routes including the China Scholarship Council (CSC), PTDF Overseas Scholarship Scheme (Nigeria), LPDP Overseas Scholarships (Indonesia), Bangabandhu Science and Technology Fellowship Trust (Bangladesh), or other sponsorships. Academic support letters are available for suitable candidates seeking funding.

To apply, interested candidates should contact Dr. Wang by email with a brief CV, academic transcripts, a short description of research interests, and funding status. The position is based in Edinburgh, Scotland, United Kingdom, at Heriot-Watt University.