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Computational Scientist in the Artificial Intelligence for Science (AIScience)

Princeton Plasma Physics Laboratory -
Princeton, NJ

Apply Now

Job Details

Full-time
$163,600 - $261,400 a year

Qualifications

AI models
Management
Scientific publications
Team leadership
Publishing papers in peer-reviewed journals
Research project technical leadership
Machine intelligence
Academic leadership
Managing teams of scientists
Doctor of Philosophy
AI implementation
Machine learning (ML) fundamentals
Artificial intelligence research

Full Job Description

Overview:

The Princeton Plasma Physics Laboratory (PPPL) seeks to fill a Computational Scientist in Artificial Intelligence For Science (AI4Science) position in the Computational Sciences Department. The successful candidate will establish and solicit long-term funding for a program focused on (a) foundational research in Artificial Intelligence and Machine Learning (AI/ML), and (b) application-oriented research in PPPL-relevant AI4Science topics. This is a leadership position that will strongly align with the Genesis Mission – a new initiatives in AI/ML for Science that has been launched by the Department of Energy (DOE). The incumbent will develop a fast-paced AI/ML program strategically aligned with DOE and other Federal Agency goals, ensuring that the research program advances and fits within PPPL Annual Laboratory Plans (ALP) goals.

Artificial Intelligence for Science and Energy represents a fundamental change in the scientific enterprise and an opportunity to provide foundational capabilities upon which to broaden PPPL’s mission. The incumbent will build new capabilities in the Computational Sciences Department to leverage this once-in-a-generation opportunity to build an AI4Science research program at PPPL. Specifically, the incumbent will address the emerging need for AI/ML in fusion, other areas of plasma physics, and computational sciences. In addition, the incumbent will work with CSD Leadership to make key hires, build core research capabilities (including training of existing staff), and design a research program to discover new methods in data assimilation, experimental prediction, control systems, and solutions to partial differential equations.

To establish and solicit long-term funding for a program focused on (a) foundational research in Artificial Intelligence and Machine Learning (AI/ML), and (b) application-oriented research in PPPL-relevant AI4Science topics.

The Computational Sciences Department at PPPL was formed to provide a focus for computational physics and engineering. We specialize in algorithms and applied mathematics, data science and learning, high-performance computing, multiscale integrated modeling, and software technology. While our current strengths reflect the traditional focus of the Laboratory on magnetic confinement fusion (MCF), with funding from the Department of Energy’s offices of Fusion Energy Sciences and the Advanced Scientific Computing Research, PPPL has always had a broad and healthy research program in areas other than MCF, including developing the theoretical and computational foundations of the dynamics and thermodynamics of naturally occurring plasmas, and more recently, in AI/ML and AI4Science.

The successful candidate will help develop the laboratory’s effort in AI/ML and AI4Science, collaborating with CSD leadership and other laboratory divisions. The candidate will help establish partnerships with Princeton University and other DOE National Laboratories. The candidate will assist in recruiting new team members, seek and secure funds to support the laboratory team, and present and publish original research in this general area. The present position comes with steady-state funding for three years.

We are looking for candidates who can build a strong research program in one or more of the following topics:

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

This position requires building close collaboration with CSD Leadership, PPPL experimentists, the PPPL Theory Department, and Princeton University researchers.

A U.S. Department of Energy National Laboratory managed by Princeton University, the Princeton Plasma Physics Laboratory (PPPL) is tackling the world’s toughest science and technology challenges using plasma, the fourth state of matter. With more than 70 years of history, PPPL is a leader in the science and engineering behind the development of fusion energy, a potentially limitless energy source. PPPL is also using its expertise to advance research in the areas of microelectronics, quantum sensors and devices, and sustainability sciences. Whether it be through science, engineering, technology or professional services, every team member has an opportunity to contribute to our mission and vision. Come join us!

Responsibilities:

Core Duties:

50% Delivering on projects (A.I. research).
30% defined research.
20% writing proposal building critical mass.

Qualifications:

Education and Experience:

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.

Working Conditions:

Day shift, on-site.

Princeton University is an Equal Opportunity Employer and all qualified applicants will receive consideration for employment without regard to age, race, color, religion, sex, sexual orientation, gender identity or expression, national origin, disability status, protected veteran status, or any other characteristic protected by law.

The University considers factors such as (but not limited to) scope and responsibilities of the position, candidate's qualifications, work experience, education/training, key skills, market, collective bargaining agreements as applicable, and organizational considerations when extending an offer. The posted salary range represents the University's good faith and reasonable estimate for a full-time position; salaries for part-time positions are pro-rated accordingly.

If the salary range on the posted position shows an hourly rate, this is the baseline; the actual hourly rate may be higher, depending on the position and factors listed above.

The University also offers a comprehensive benefit program to eligible employees. Please see this link for more information.

Please be aware that the Department of Energy (DOE) prohibits DOE employees and contractors from participation in certain foreign government talent recruitment programs. All PPPL employees are required to disclose any participation in a foreign government talent recruitment program and may be required to withdraw from such programs to remain employed under the DOE Contract.

Standard Weekly Hours: 40.00 Eligible for Overtime: No Benefits Eligible: Yes Probationary Period: 180 days Essential Services Personnel (see policy for detail): No Physical Capacity Exam Required: No Valid Driver's License Required: No : #LI-BH1 Salary Range: $163,600 to $261,400

Apply Now

Refine Your Search

models models quantum algorithm quantum algorithm algorithms quantum algorithm jobs

Computational Scientist in the Artificial Intelligence for Science (AIScience)

Lead Research Scientist

Research Associates - National Security Initiatives

Senior Software Engineer, Back-end (Boulder Opal)

Member of Technical Staff, Microsoft Robotics (Robotics Simulation)

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Senior Machine Learning Engineer – Engineering Intelligence Systems

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Quantum Cryptography Engineer

Analytics & Data Science

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AI Solutions Architect

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