PhD: AI-Driven Particle Thermal Energy Storage Modeling

The University of Manchester; Manchester; On-site; GBP 20,000 - 24,000; Full time

Benefits offered by this job

Tax-free stipend

Fees fully covered

Job summary

The University of Manchester invites applications for a 3.5-year PhD Studentship in Mechanical Engineering, focused on high-fidelity modelling, CFD, and physics-informed AI for particle-based long-duration energy storage under variable renewables.

The successful candidate will develop multi-physics CFD models and PINNs, study particle dynamics and turbulent heat transfer, and enable real-time monitoring and adaptive control.

Qualifications

• A strong background in mechanical/energy engineering or related discipline is expected.
• Experience with CFD/ multi-physics modelling is advantageous.

Responsibilities

• Develop multi-physics CFD models to study particle dynamics and turbulent heat transfer.
• Integrate PINNs and AI-enabled digital twins for real-time monitoring and adaptive control.

Skills

Particle dynamics

CFD

Physics-Informed Neural Networks

AI for energy storage

Digital twin

Computational modelling

Education

2.1 honours degree or master's in relevant science/engineering

Job description

The University of Manchester invites applications for a 3.5-year PhD Studentship in Mechanical Engineering, focused on high-fidelity modelling, CFD, and physics-informed AI for particle-based long-duration energy storage under variable renewables.

The successful candidate will develop multi-physics CFD models and PINNs, study particle dynamics and turbulent heat transfer, and enable real-time monitoring and adaptive control.