Publisher
source

Nanzhe Wang

11 months ago

PhD in AI-Enhanced Geothermal Energy Modelling and Decision-Making Heriot-Watt University in United Kingdom

Degree Level

PhD

Field of study

Computer Science

Funding

The PhD studentship is fully funded for 3.5 years, covering tuition fees and providing a stipend. Additional research training support is available, including computational resources, conference participation, and field trips. Funding is available for UK home students; limited funding for international students (only one can be shortlisted).

Deadline

Expired

Country flag

Country

United Kingdom

University

Heriot-Watt University

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Keywords

Computer Science
Machine Learning
Environmental Science
Artificial Intelligence
Heat Transfer
Earth Science
Uncertainty Analysis
Reinforcement Learning
Reservoir Engineering
Geothermal Engineering
Optimisation
Physics

About this position

This fully funded PhD opportunity at Heriot-Watt University, offered through the Iapetus Doctoral Training Partnership, focuses on the integration of artificial intelligence and geoscience for the advancement of geothermal energy systems. The project, titled 'Intelligent Geothermal Energy Development: AI-Enhanced Modelling and Decision-Making Under Uncertainty,' aims to develop innovative AI-driven frameworks for modelling, exploration, and management of geothermal reservoirs. The research combines scientific machine learning, reinforcement learning, surrogate reservoir modelling, and uncertainty quantification to optimize geothermal reservoir management and exploration strategies. The interdisciplinary supervisory team includes experts from Heriot-Watt University, Durham University, and Queen Mary University of London, covering AI, subsurface engineering, heat and mass transfer, and environmental science. The student will receive comprehensive training in AI, machine learning, numerical simulation, and geoscience, with opportunities for field trips, conference participation, and engagement with industry partners. The project supports the UK Geothermal Strategy and AI sector objectives, with broader applications in subsurface technologies such as carbon storage and mineral exploration. Funding covers tuition, stipend, and research support for 3.5 years. UK home students should apply by January 5th, while international applicants must contact the primary supervisor by December 8th due to limited places. Applicants should have a strong background in a relevant discipline and a keen interest in interdisciplinary research. For more details and to apply, visit the provided links or contact Dr. Nanzhe Wang for informal enquiries.

Funding details

The PhD studentship is fully funded for 3.5 years, covering tuition fees and providing a stipend. Additional research training support is available, including computational resources, conference participation, and field trips. Funding is available for UK home students; limited funding for international students (only one can be shortlisted).

What's required

Applicants should have a strong academic background in a relevant discipline such as geoscience, engineering, computer science, physics, or mathematics. Experience or interest in artificial intelligence, machine learning, numerical modelling, or geothermal energy is highly desirable. UK home students are eligible; only one international student can be shortlisted, so international applicants should contact the primary supervisor before applying. Good communication skills and motivation for interdisciplinary research are expected.

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Professors

Assistant Professor

University Name
Country flag

United Kingdom

Research Interests

Hydrogeology

Artificial Intelligence

Geothermal Engineering

Uncertainty Analysis

Earth Science

Dr.

University Name
Country flag

United Kingdom

Research Interests

Artificial Intelligence

Reservoir Engineering

Geothermal Engineering

Machine Learning

Uncertainty Analysis

Professor

University Name
Country flag

United Kingdom

Research Interests

Artificial Intelligence

Reservoir Engineering

Computer Science

Uncertainty Analysis

Heat Transfer

Professor

University Name
Country flag

United Kingdom

Research Interests

Reservoir Engineering

Artificial Intelligence

Geothermal Engineering

Uncertainty Analysis

Computer Science