PhD Studentship: Physics-informed AI for the Thermodynamic Co-design of Emerging Cooling Technologies

University of Birmingham; Birmingham; On-site; GBP 19,000 - 21,000; Full time

Benefits offered by this job

Funding

Job summary

The University of Birmingham invites applications for a PhD project to develop a physics-informed artificial intelligence framework for systematic design and optimisation of alternative cooling mechanisms and their refrigeration cycles. The project combines thermodynamic modelling, machine learning, and multi-objective optimisation.

You will join the School of Chemical Engineering at Birmingham, gaining hands-on experience in thermal energy experiments, simulation, and data analysis, with

Qualifications

• A good bachelor’s degree (first or upper second-class honours) or an MSc in Mechanical Engineering, Chemical Engineering, Materials Science, Physics, Energy or other relevant disciplines.
• Strong interest in thermal energy engineering with beneficial experience in combining computational and experimental work.
• Ability to work independently and in a research team, both autonomously and collaboratively.

Responsibilities

• Develop a physics-informed AI framework for systematic design and optimisation of cooling mechanisms and their refrigeration cycles.
• Integrate thermodynamic modelling, machine learning, and multi-objective optimisation to identify high-potential design options.
• Carry out experimental investigations to validate models and refine the computational framework.
• Balance experimental and computational work according to background and interests.

Skills

Thermal energy engineering

Computational & experimental work

Data analysis

Project management

Education

Bachelor’s degree or MSc in Mechanical/Chemical Eng/Materials/Physics/Energy

Job description

Cooling demand is rapidly increasing due to economic development and climate change-induced temperature increase. Vapour compression cycle has been the main technology to satisfy the global cooling demand, from household refrigerators and room air conditioners to industrial size chillers. Their environmental impacts from energy input and refrigerants leakage have pushed the development of alternative cooling technologies, such as sorption-based and solid-state caloric cooling. However, the deployment of these technologies has been hindered by limited cooling capacity, thermodynamic irreversibilities, inefficient heat transfer, and impractical driving forces.

This PhD project will focus on developing a physics-informed artificial intelligence framework for systematic design and optimisation of alternative cooling mechanisms and their corresponding refrigeration cycles. The project will integrate thermodynamic modelling, machine learning, and multi-objective optimisation to identify high potential combinations of cooling mechanisms, refrigeration cycles, and operating conditions. Experimental investigations will validate selected combinations and refined the models. The successful candidate will have the opportunity to allocate and balance the experimental and computational work depending on their background and interests.

At Birmingham, the candidate will join the School of Chemical Engineering which hosts several world-leading cooling-related research centres, including the Centre for Sustainable Cooling and Birmingham Centre for Energy Storage. The candidate will develop a wide range of skills in thermal energy experiments, simulation, optimisation, data analysis, and project management which are applicable to a wide range of future career options.

Applications will be accepted until 31 January 2027 but the position will be filled as soon as an appropriate candidate is found. For informal enquiries about the project, please contact Dr. Renaldi Renaldi at [e-mail].

Entry requirements

A good bachelor’s degree (first or upper second-class honours degree) or an MSc degree in Mechanical Engineering, Chemical Engineering, Material Science, Physics, Energy or other relevant disciplines. A strong interest in thermal energy engineering and prior experience in combining computational and experimental work would be beneficial. Candidates should be able to work both independently and collaboratively as part of a research team.

Funding notes:

This project is funded by the University of Birmingham and is open to UK/home students. Funding opportunity for exceptional international student is a possibility. For more information on postgraduate research at the University of Birmingham please visit: https://www.birmingham.ac.uk/study/postgraduate/research.

https://admissions.bham.ac.uk/course-finder-landing-page/?code=EPS020