PhD Studentship: Quantum-Inspired Machine Learning (QIML) for Net-Zero Energy Optimisation

This exciting PhD opportunity aims to explore the cutting-edge intersection of quantum computing and machine learning to develop novel algorithms across various applications in energy sustainability and systems.

Overview

• Qualification type: PhD
• Subject area: Thermal Energy, Physics, Mathematics, Quantum Computing, Machine Learning, AI
• Location/Campus: Hatfield/College Lane
• Start date: 1 May 2024 or as soon as thereafter
• Closing application date: 15th March 2024
• Full-time/ part-time availability: Three years, full time

Project outline

The growing recognition of quantum computing's potential as a catalyst for change in the energy sector highlights the transformative role of Quantum Machine Learning (QML). In the realm of energy systems, QML presents opportunities to optimise power systems, refine energy distribution, enhance renewable energy efficiency, and curtail carbon emissions. One notable application lies in the potential revolution of processes related to hydrogen fuel cells. Through the application of quantum algorithms, we can navigate vast solution spaces to identify novel materials with optimal properties for fuel cells. Quantum machine learning techniques further offer the means to optimise crucial parameters in fuel cell operation, promising significant advancements in performance and durability.

This PhD project aims to spearhead the development and implementation of novel Quantum-Inspired Machine Learning (QIML) algorithms and models. While the primary focus is on fuel cell performance and durability, the project remains open to exploring innovative applications in energy systems, providing solutions to real-world challenges and paving the way for more efficient and sustainable energy solutions.

Supervisors

Dr Christos Kalyvas, [email protected]

Dr Xing Liang, [email protected]

Entry requirements

Essential

• A first or upper second-class degree (or equivalent) in a relevant discipline such as, Mechanical/Chemical Engineering, Energy Sustainability, Physics, Mathematics, and Computer Science, preferably with postgraduate qualifications;
• Good analytical and programming/simulation skills in scientific computing languages such as Python;
• Ability to work collaboratively and manage time independently to meet deadlines;
• Excellent oral and written English communication skills.
• Overseas applicants to have an IELTS (English proficiency) score of 6.5 or above (if they get selected for the studentship).

Desirable

• Prior experience with quantum computing and machine learning is highly desirable;
• Publications in high-impact international journals and conferences;
• Familiarity with energy system or fuel cell;
• Experience working within the industry will be an advantage.

Eligibility

The studentship is open to UK/EU and international applicants.

How to apply

For informal enquires please email: Prof. Hongwei Wu ([email protected] ), the principal supervisor.

Please download and complete an application form

In section 11 you must provide a comprehensive personal statement of up to 500 words describing your motivation to do research on this project at the University of Hertfordshire.

Please also send with your application form:

• A research proposal not exceeding 1 page
• Two academic references
• Copies of qualification certificates and transcripts
• Certification of English language competence (minimum IELTS 6.5 or equivalent) for candidates for whom English is not their first language.

Please email the application form and supporting document to Ms Amy Bird at [email protected]

Interview dates: 22 March 2024 or as soon as thereafter

Funding information

Fully funded for UK, EU and international students.The studentship is for 3 years and covers the full cost of tuition fees and an annual tax-free stipend at UKRI rate (currently £18,622 for 2023/24).