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Massachusetts Institute of Technology Postdoctoral Associate in Cambridge, Massachusetts

Postdoctoral Associate

  • Job Number: 21487

  • Functional Area: Research - Engineering

  • Department: Nuclear Science and Engineering

  • School Area: Engineering

  • Employment Type: Full-Time

  • Employment Category: Exempt

  • Visa Sponsorship Available: Yes

  • Schedule:

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    Information on MIT’s COVID-19 vaccination requirement can be found at the bottom of this posting.

POSTDOCTORAL ASSOCIATE, Nuclear Science and Engineering, to develop a software/hardware framework to scale NAA (neutron activation analysis) for measuring heavy metal concentrations in a large number of environmental samples. The framework includes the automation of measurements and sample preparation as well as machine learning methods for automated gamma-ray spectral identification and uncertainty quantification of metal concentrations in NAA. This software workflow involves the quantification of various factors and processes, including sample conditions, irradiation condition and duration, calibration samples, and post-irradiation gamma spectroscopy analysis. Will develop a suite of algorithms for automated gamma-ray spectral identification and uncertainty quantification of metal concentrations in NAA, and/or design the automation hardware for processing a large numbers of samples; work with interdisciplinary researchers, including environmental informatics and nuclear science; author peer-reviewed conference or journal papers; and perform other duties as needed. Will be encouraged to explore own ideas within the scope of the project. A journal paper is expected to document the algorithms and application to actual samples.

Job Requirements

REQUIRED: Ph.D. in physics, nuclear engineering, health physics, environmental sciences/engineering, data science, applied mathematics, computer science, or related technical disciplines; understanding of basic nuclear physics and gamma spectroscopy; ability to analyze data and extract patterns and correlations using appropriate data mining methods and to work collaboratively with a multidisciplinary team to integrate diverse datasets. PREFERRED: familiarity with libraries, programming, frameworks, or workflow tools that enable data analytics and machine learning (e.g., NumPy, Pandas, Scikit-learn, Keras, Tensorflow, Jupyter Notebooks); understanding of statistical methods and machine learning; familiarity with neutron activation analysis; and demonstrated record of publications and conference presentations. Job #214876/27/22