Associate Research Physicist

Location
New Jersey, United States
Posted
Monday, 19 October 2020
End of advertisement period
Saturday, 19 December 2020
Ref
12057
Contract Type
Permanent
Hours
Full Time

Department   PPPL Office of the Director
Category   Research and Laboratory
Full-Time / Part-Time   Full-Time

Overview

Funded by a new Department of Energy FES initiative on Scientific Machine Learning and Artificial Intelligence for Fusion Energy Sciences, the Princeton Plasma Physics Laboratory (PPPL) is seeking a research associate to develop machine learning algorithms for controlled nuclear fusion. PPPL is a world leading Department of Energy plasma physics laboratory focusing on fusion energy research and is managed by Princeton University. This is a multi-institutional project with components from PPPL, Princeton University, Carnegie Mellon University, University of Wisconsin, and SLAC. Machine learning is expected to play an integral role in plasma control systems in order to achieve stable plasma burn for fusion energy. This project will span the application of machine learning methods to real-time diagnostic analysis, predictive modeling of the evolution of the tokamak system, and development of machine learning control approaches, e.g., reinforcement learning.

The successful candidate will work with the multi-institutional team to develop reduced/accelerated models of plasma behavior and approaches to manipulate available actuators to optimize experiment performance. The appointment will be based in Princeton, but experimental testing of algorithms is planned for the DIII-D tokamak in San Diego.

Responsibilities

The successful candidate will work with the team to develop machine learning accelerated approaches for plasma equilibrium and profile evolution in DIII-D discharges. The candidate will use these models to develop and test real-time control strategies designed to optimize performance. Relevant methods include deep learning, reinforcement learning, Bayesian optimization, Gaussian processes, dynamic systems modeling and controls. The methods will be tested first using PPPL's simulations of tokamaks and those that are successful will be tested on the real devices.

We are looking for a highly motivated scientist, a team player and an excellent communicator who will collaborate closely with the on-site team at PPPL, the other institutions that are part of the project, and the DIII-D team.

The successful candidate will be based in Princeton and is expected to occasionally travel to DIII-D (San Diego) and/or other national and international fusion facilities for joint experiments, workshops and conferences.

Qualifications

Education and Experience: 

  • Applicants should have a Ph.D. in control engineering, machine learning, plasma physics, or related fields.
  • Preference will be given to candidates with experience in tokamak physics, machine learning for dynamic systems, and optimization.

Knowledge, Skills and Abilities: 

  • Familiarity with machine learning approaches for modeling complex time-dependent, spatially distributed systems is required.
  • Experience with optimization, and/or control algorithm design is desired.
  • Excellent software development skills in Python and/or C/C++, MATLAB/Simulink
  • Excellent presentation, writing, and communication skills

Princeton University is an Equal Opportunity/Affirmative Action 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. EEO IS THE LAW

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.

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