Research Fellow – Plasma Physics for Fusion Energy

About the role

The School of Physical and Mathematical Sciences at NTU Singapore is seeking a Research Fellow to advance machine‑learning‑driven modelling of plasma turbulence in magnetically confined fusion devices. The successful candidate will join the Division of Physics and Applied Physics, work on AI‑based reduced‑order models, and collaborate with international partners such as CEA, France.

Key responsibilities

• Apply advanced machine‑learning algorithms, including deep and physics‑informed neural networks, to large datasets from gyrokinetic and fluid simulations.
• Develop and train reduced‑order models that capture turbulent transport dynamics in tokamak plasmas for fast predictive capability.
• Benchmark AI‑driven models against experimental observations and conventional simulations.
• Collaborate with overseas scientific teams to integrate AI models into broader fusion research programmes.

Required profile

• PhD in Physics, Applied Mathematics, Computational Science or a related discipline.
• Strong background in machine learning applied to scientific data.
• Familiarity with plasma physics, especially turbulence and transport (desired).
• Proven record of scientific publications or software contributions.
• Excellent communication skills for presenting results and writing papers.

Required skills

• Python programming.
• TensorFlow or PyTorch.
• Scikit‑learn.