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Nanzhe Wang

8 months ago

PhD in AI-Enabled Geoenergy & Subsurface Systems (Physics-based Modelling, AI, Geoenergy, Subsurface Engineering) Heriot-Watt University in United Kingdom

Degree Level

PhD

Field of study

Computer Science

Funding

Funding is available through external scholarships such as the China Scholarship Council (CSC) for visiting PhD students from China, PTDF Overseas Scholarship Scheme (Nigeria), LPDP Overseas Scholarships (Indonesia), Bangabandhu Science & Technology Fellowship Trust (Bangladesh), and other national, governmental, or industrial sponsorships. Academic support letters for funding applications are provided to eligible candidates. No direct funding from the supervisor is mentioned.

Deadline

Expired

Country flag

Country

United Kingdom

University

Heriot-Watt University

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Keywords

Computer Science
Machine Learning
Environmental Science
Earth Science
Uncertainty Analysis
Data Assimilation
Digital Twin Technology
Geothermal Engineering

About this position

Professor Nanzhe Wang at Heriot-Watt University is recruiting PhD students for research in AI-enabled geoenergy and subsurface systems. The research focuses on integrating physics-based modelling and artificial intelligence to address uncertainty quantification, data assimilation, and decision-making in subsurface energy systems. Applications include geological carbon storage, geothermal energy, hydrocarbon reservoirs, and groundwater systems, all of which are critical for supporting the energy transition and achieving net-zero goals.

PhD research topics include hybrid physics–AI modelling and inversion for geoenergy and subsurface flow systems, multi-source and multi-scale data assimilation, digital twins for subsurface energy systems using generative AI and surrogate models, uncertainty-aware decision-making and control for subsurface operations, and active learning for efficient inversion and optimisation. The ideal candidate will have a background in engineering, data science, geoscience, applied mathematics, physics, or computer science, with programming skills in Python, MATLAB, PyTorch, or TensorFlow, and an interest or experience in subsurface systems, geoenergy, and AI/ML.

Funding is available through various external scholarships, including the China Scholarship Council (CSC) for Chinese students, PTDF Overseas Scholarship Scheme (Nigeria), LPDP Overseas Scholarships (Indonesia), Bangabandhu Science & Technology Fellowship Trust (Bangladesh), and other national or industrial sponsorships. Eligible candidates can receive academic support letters for their funding applications. To apply, candidates should email Professor Wang with their CV, academic transcripts, a summary of research interests, and funding status. This opportunity is ideal for students interested in AI, earth science, environmental science, and the application of advanced computational methods to energy and sustainability challenges.

Funding details

Funding is available through external scholarships such as the China Scholarship Council (CSC) for visiting PhD students from China, PTDF Overseas Scholarship Scheme (Nigeria), LPDP Overseas Scholarships (Indonesia), Bangabandhu Science & Technology Fellowship Trust (Bangladesh), and other national, governmental, or industrial sponsorships. Academic support letters for funding applications are provided to eligible candidates. No direct funding from the supervisor is mentioned.

What's required

Applicants should have a background in engineering, data science, geoscience, applied mathematics, physics, or computer science. Interest or experience in subsurface systems, geoenergy, and AI/machine learning is required. Programming skills in Python, MATLAB, PyTorch, or TensorFlow are expected. Candidates must prepare a CV, academic transcripts, a short summary of research interests, and indicate their funding status. Eligibility for specific funding routes (e.g., CSC, PTDF, LPDP, Bangabandhu Fellowship) may apply based on nationality.

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Professors

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University Name
Country flag

United Kingdom

Research Interests

Statistics

Hydrogeology

Earth Science

Environmental Science

Uncertainty Analysis