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Chao Long

Just Landed

3 days ago

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 studentship in a four-year dual PhD programme. 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.

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Country

United Kingdom

University

University of Liverpool

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Keywords

Computer Science
Environmental Science
Electrical Engineering
Electric Vehicle
Batteries
Photovoltaic
Renewable Energy Systems
ML

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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 about two years at each university, gaining experience across the UK and Taiwan.

Funding includes tuition fee coverage, a UKRI-rate stipend while in Liverpool (£21,805 per year from October 2026), and NT$15,000 per month while in Taiwan. This is 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 interest in machine learning.

To apply, email your CV and a brief statement of research interests to [email protected].

Funding details

Fully funded Home-fee studentship in a four-year dual PhD programme. 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.

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].

More information can be found here

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