Computational Scientist in the Artificial Intelligence for Science (AIScience)

Princeton Plasma Physics Laboratory•Princeton, NJ

20h•Onsite

About The Position

The Princeton Plasma Physics Laboratory (PPPL) seeks a Computational Scientist in Artificial Intelligence For Science (AI4Science) within the Computational Sciences Department. This leadership position involves establishing and securing long-term funding for a program focused on foundational research in Artificial Intelligence and Machine Learning (AI/ML) and application-oriented research in PPPL-relevant AI4Science topics. The role is strongly aligned with the Department of Energy's (DOE) Genesis Mission, a new initiative in AI/ML for Science. The incumbent will develop a fast-paced AI/ML program strategically aligned with DOE and other Federal Agency goals, ensuring it fits within PPPL Annual Laboratory Plans (ALP) goals. This position offers a unique opportunity to build new capabilities in the Computational Sciences Department, leveraging AI/ML to broaden PPPL’s mission in fusion, plasma physics, and computational sciences. Responsibilities include working with CSD Leadership to make key hires, build core research capabilities (including staff training), and design a research program to discover new methods in data assimilation, experimental prediction, control systems, and solutions to partial differential equations. The candidate will also establish partnerships with Princeton University and other DOE National Laboratories, assist in recruiting new team members, seek and secure funds, and present and publish original research. The position comes with steady-state funding for three years. PPPL, managed by Princeton University, is a U.S. Department of Energy National Laboratory focused on plasma science, fusion energy, microelectronics, quantum sensors, and sustainability sciences.

Requirements

Ph.D. in Computer Science, Mathematics, Applied Mathematics, or a related field with core training in foundational & applied aspects of AI/ML.
Minimum 15 years of professional experience in an academic, scientific, or R&D environment.
A proven track record of publishing original results in peer-reviewed scientific journals.
Demonstrated scientific leadership and collaboration experience.

Nice To Haves

Ability to build a strong research program in Machine Learning for Digital Twins, Foundation Models, and surrogates.
Ability to build a strong research program in Inference tools for interpretive analysis of experimental and simulation data.
Ability to build a strong research program in Foundational research in Machine Learning for partial differential equations (PDEs).
Ability to build a strong research program in Innovative algorithmic and methodological approaches to AI-augmented HPC application acceleration.
Ability to build a strong research program in Advanced systems and software for AI-augmented High-Performance Computing at scale.
Ability to build a strong research program in Machine-learning-driven control systems control large and complex experiments in real-time, to avoid “dangerous” conditions (disruption avoidance) using feedback systems.
Ability to build a strong research program in Scalable hybrid AI-HPC workflows using advanced capabilities.