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Massachusetts Institute of Technology Postdoctoral Associate, Theory Group in Cambridge, Massachusetts

Postdoctoral Associate, Theory Group

  • Job Number: 19005

  • Functional Area: Research - Scientific

  • Department: Plasma Science and Fusion Center

  • School Area: VP Research

  • Employment Type: Full-Time

  • Employment Category: Exempt

  • Visa Sponsorship Available: Yes

  • Schedule:

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    Working at MIT offers opportunities, an environment, a culture – and benefits – that just aren’t found together anywhere else. If you’re curious, motivated, want to be part of a unique community, and help shape the future – then take a look at this opportunity.

POSTDOCTORAL ASSOCIATE, Plasma Science and Fusion Center (PSFC)-Theory Group, to assume a position in machine learning applied to the development of fast surrogate models of computationally intensive radio frequency (RF) heating and current drive simulations. These surrogate models will enable enhanced real-time plasma control, better interpretation of experimental data, and efficient time-dependent integrated modeling of advanced tokamak plasmas. Predictive capability of RF wave heating and current drive is critical for present-day fusion experiments and to the design and construct a fusion power plant. The “advanced tokamak” (AT) concept is a leading candidate for a steady-state fusion power plant. The AT makes use of the pressure-gradient-driven bootstrap current to sustain a majority of the required plasma current, augmented by non-inductive current drive actuators. Control of the RF heating and current drive profiles, through varying the launched power and wavenumber of the system, is one of the few direct current profile control knobs available on a tokamak. Although RF simulation tools are sophisticated and accurate, the computational resources required are considerable and widespread use in parametric scenario scoping studies, time-dependent modeling, and real-time control of experiments will benefit from reducing the time required for simulation modeling, which this project aims to accomplish. Will collaborate on developing solutions to forward and inverse problems arising in the development of fast surrogate models for complex computer codes. May also manage training data, develop supervised learning pipelines, and develop classifiers.

Job Requirements

REQUIRED: Ph.D. in applied math, computer science, physics or related discipline; and expertise with machine learning methods such as various neural network techniques and experience applying these methods to modeling of physics systems. Job #19005

Application material should include a CV, publication list, and statement of research interests. Applicants should also arrange for three letters of reference to be sent to John Wright at

This is a one-year appointment with the possibility of a one or two one year re-appointment assuming satisfactory performance and the availability of funds.10/8/20