University of Liverpool
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Fully funded PhD in Machine Learning for Community Energy Management University of Liverpool in United Kingdom
Degree Level
PhD
Field of study
Computer Science
Funding
Fully funded Home-fee PhD studentship. Tuition fees are covered. Stipend is at the UKRI rate while in Liverpool (£21,805 per year from October 2026) and NT$15,000 per month while in Taiwan. The student will spend approximately two years at each university in the four-year dual PhD programme.
Country
United Kingdom
University
University of Liverpool

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About this position
Fully funded PhD opportunity in Machine Learning for Community Energy Management at the University of Liverpool in a four-year dual PhD programme with National Tsing Hua University, Taiwan.
The project focuses on developing machine learning methods to coordinate solar PV, batteries, and electric vehicles in community energy systems. The student will spend approximately two years at each university, gaining experience across both institutions and research environments.
Funding includes tuition fee coverage plus a stipend at the UKRI rate while in Liverpool (£21,805 per year from October 2026) and NT$15,000 per month while in Taiwan. This is specifically a Home-fee studentship, so applicants must be eligible for UK Home-fee status.
Preferred background: an MSc in electrical engineering, energy systems, computer science, or a related field, with an interest in machine learning. Interested candidates should email a CV and a brief statement of research interests to [email protected].
Funding details
Fully funded Home-fee PhD studentship. Tuition fees are covered. Stipend is at the UKRI rate while in Liverpool (£21,805 per year from October 2026) and NT$15,000 per month while in Taiwan. The student will spend approximately two years at each university in the four-year dual PhD programme.
What's required
Applicants should be eligible for UK Home-fee status and ideally hold an MSc in electrical engineering, energy systems, computer science or a related field. An interest in machine learning is preferred.
How to apply
Email your CV and a brief statement of research interests to [email protected]. If interested, you may also contact Lin Cao or Dr Long directly.
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