We are seeking a machine learning researcher to aid in the development of a new analysis platform for nuclear non-proliferation. Broadly speaking, nuclear non-proliferation seeks to keep nuclear weapons away from people who shouldn’t have them.

This position will help develop and implement new machine learning and/or deep learning algorithms for uncertainty quantification of non-proliferation data. This data may stem from a variety of real and simulated sources. Real time algorithms to validate and interpret incoming data are highly-desirable.

As a research position, the primary goal of the applicant will be to write publications demonstrating the results of interesting and new investigations.

Specific area of work will be tailored to applicant and applicants interests.

strong>Applicant must be eligible for U.S. national security clearance.

Positions

This job is open for the following positions:
  • Scientific Software Developer
  • Graduate Student
  • Post-doctoral Scholar

Salary and compensation will be based on prior work experience.

Background


Background in at least one of the following fields is requested:
  • Data Science
  • Data Engineering
  • Applied Mathematics
  • Nuclear Engineering
  • Mechanical Engineering
  • Computer Science
  • Computer Engineering
  • Physics
  • Or similar

Expertise


No prior nuclear engineering knowledge is strictly required, though a desire to learn-as-you-go is needed.

Applicable software development skills include knowledge of:

  • At least one programming language, preferred languages include:
    • Python
    • Haskell
    • C++
  • git or hg, or other version control system

  • Test-driven development
  • Other software development best practices.

Potentially useful other software development skills include:

  • High performance computing
  • Cloud computing (high throughput computing)
  • Data Science

Application Information


Status: This position is currently CLOSED.

Start Date: ASAP

Posting Date: July 1, 2015

Contact: Please send CV or resume AND a code sample (link or file) to Prof. Scopatz at scopatzATcec.sc.edu.