Associate Research Physicist

Job description

Overview

The Princeton Plasma Physics Laboratory is a world-class fusion energy research laboratory managed by Princeton University for the U.S. Department of Energy’s Office of Science. PPPL is dedicated to developing the scientific and technological knowledge base for fusion energy. The Laboratory advances the fields of fusion energy and plasma physics research to develop the scientific understanding and key innovations needed to realize fusion as an energy source for the world.

 

The Princeton Plasma Physics Laboratory (PPPL) is seeking a highly motivated scientist with a strong computational and applied math background in order to accelerate the radio-frequency (RF) wave field solver with artificial intelligence/machine learning (AI/ML) technology.

 

Princeton Plasma Physics Laboratory is a world-class fusion energy research laboratory. The RF wave heating, a major tool to heat plasmas and to maintain fusion reactions, injects high frequency RF waves using geometrically complicated antenna closely located to the plasma, and waves are absorbed by plasma through linear and non-linear processes. Computing the RF wave field in hot plasma requires solving the large scale Vlasov-Maxwell type equation defined in 3D. Accelerating such simulation codes with modern hardware and algorithms are crucial to extend our simulation capability to up-coming fusion reactors such as ITER and to utilize it on interpreting the experimental data from existing fusion devices.

 

The successful candidate is expected to work with AI/ML engineers and ASCR scientists at BNL and LLNL, and will be responsible for adopting AI/ML algorithms to generate a matrix preconditioner for the RF propagation problem. This problem is expressed as an indefinite Maxwell type partial differential equation (PDE) and its discretized linear system depends on the target plasma parameter profiles. The candidate will work on our Petra-M finite element analysis platform to create the training data set (pairs of linear system and solutions), develop DNN models to generate pre-conditioner to solve the PDE for new profiles, and test its efficiency. Close communication with RF SciDAC plasma physicists is anticipated in order to verify and apply the developed algorithms to a wide range of RF heating experiments.

Responsibilities
  • Ph.D. in applied math, plasma physics or a closely related discipline is required
  • Experience in computational physics code development is required
    • Fluency in the scientific programing languages such as C++ and Python
    • Familiarity with AI/ML libraries such as PyTORCH
    • Collaborative experience with code authors and other software engineers.
  • Desirable to have an experience in
    • Linear solvers and pre-conditioners
    • Wave physics in plasmas
Qualifications

Education and Experience: 

  • Ph.D. in applied math, plasma physics or a closely related discipline

Knowledge, Skills and Abilities: 

  • Fluency in the scientific programing languages such as C++ and Python
  • Familiarity with AI/ML libraries such as PyTORCH
  • Collaborative experience with code authors and other software engineers

Other: 

  • Qualified applicants should apply and send a curriculum vitae and bibliography. Three letters of recommendation will be requested from finalists

 

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.

Standard Weekly Hours40.00Eligible for OvertimeNoBenefits EligibleYesEssential Services Personnel (see policy for detail)NoPhysical Capacity Exam RequiredNoValid Driver's License RequiredNo

 

 

 

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Job No:
Posted: 11/13/2020
Application Due: 11/12/2021
Work Type:
Salary: