Pacific Fusion is hiring a
PhD Intern

Summary

Join Pacific Fusion's team as a research intern to contribute to the development of machine learning systems for fusion target design. You will collaborate with computational physicists and designers to accelerate simulations, read literature, design and implement novel ML architectures, and produce reports.

Requirements

• Currently in the process of obtaining a PhD in Machine Learning, computational physics, plasma physics, or related fields
• Hands-on and mature experience in deep learning algorithms, techniques, and frameworks such as PyTorch and JAX
• Software development knowledge and experience in Python and the relevant scientific and computational stack (numpy, scipy, matplotlib, etc)
• Experience on applying machine learning to model interesting physical problems
• An interest in pushing the state of the art on problems at the intersection of physics and machine learning, including building surrogate models of physical systems, learning and optimizing PDEs, unsupervised learning for physical data, automatic differentiation, bayesian inference, and so on
• Proven track record of publications at ML venues such as NeurIPS, ICML, ICLR, TMLR, and/or in top-tier physics journals

Responsibilities

• Carry out research towards developing novel ML methods for accelerating expensive CFD-like simulations used for fusion target design
• Read through ML and physics literature that is relevant to your project
• Design, implement and train novel ML architectures
• Produce internal and/or external reports of your work in the form of whitepapers or publications to top-tier research venues