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Scientist - Nuclear Reaction Modeling & Evaluation

Job ID 3439 Date posted 10/27/2022

Brookhaven National Laboratory (www.bnl.gov) delivers discovery science and transformative technology to power and secure the nation’s future. Brookhaven Lab is a multidisciplinary laboratory with seven Nobel Prize-winning discoveries, 37 R&D 100 Awards, and more than 70 years of pioneering research. The Lab is primarily supported by the U.S. Department of Energy’s (DOE) Office of Science. Brookhaven Science Associates (BSA) operates and manages the Laboratory for DOE. BSA is a partnership between Battelle and The Research Foundation for the State University of New York on behalf of Stony Brook University. BSA salutes our veterans and active military members with careers that leverage the skills and unique experience they gained while serving our country. Our organization fully supports service members transitioning from active duty to civilian life and pledge’s our commitment to actively hire veterans of the U.S. Armed Forces. Military personnel who have been formally trained or have relevant experience obtained while in service may meet educational requirements and are encouraged to apply for job opportunities at BSA.

Organizational Overview

Brookhaven National Laboratory’s Nuclear Science and Technology Department conducts research and development related to nuclear technologies (reactors and accelerator-driven systems), reliability and risk assessment, and advanced modeling techniques for reactor simulation and energy systems.

The Department serves as a resource in these and related areas to support the missions of the Department of Energy (DOE), the Nuclear Regulatory Commission (NRC), and other national and international organizations. With a world-class staff of professionals with expertise in a broad range of areas related to the design and analyses of commercial, research and advanced nuclear systems, Brookhaven’s capabilities and facilities are also available to support and execute experiments in support of these missions. The National Nuclear Data Center (NNDC), a part of BNL’s Nuclear Science and Technology Department, is the lead unit of the US Nuclear Data Program (USNDP), whose mission is to provide current, accurate, authoritative data for workers in pure and applied areas of nuclear science and engineering. This is accomplished primarily through the compilation, evaluation, dissemination, and archiving of extensive nuclear datasets. USNDP also addresses gaps in the data, through targeted experimental studies and the use of theoretical models.

The NNDC is responsible for the Nuclear Science Reference (NSR), eXperimental Unevaluated Nuclear Data List (XUNDL), Evaluated Nuclear Structure Data File (ENSDF) and Evaluated Nuclear Data File (ENDF) libraries as well as the Nuclear Data Sheets journal.   The NNDC is also the main contributor to the EXFOR library.  Web dissemination of these libraries is also one of the main activities at the NNDC.

In addition to the libraries and web efforts, the NNDC staff are involved in research relating to nuclear structure, nuclear reactions, nuclear astrophysics as well as nuclear non-proliferation and forensics.

Nuclear Data activities started at BNL in 1952 under the Brookhaven Neutron Cross Section Compilation Group, changed to the Sigma Center in 1961, which became the National Neutron Cross Section Center in 1967 and finally NNDC in 1977, providing nearly 70 years of nuclear data expertise to nuclear science and technology professionals.

Position Description

The National Nuclear Data Center has two opportunities for scientists in nuclear reaction evaluations and research. In this role you will be expected to work on all aspects of the Evaluated Nuclear Data File (ENDF) and/or the EXFOR (EXchange FORmat) library.  ENDF is the most widely used low energy reaction database in nuclear applications, needed in reactor modeling and licensing, safeguards, shielding, space radiation effects, etc.  EXFOR is the premier database of experimental nuclear reaction data and provides the experimental grounding for the ENDF evaluations.  This work includes the compilation and evaluation of nuclear reaction and decay data, as well as experimental or theoretical research aligned with ENDF priorities. This position has a high level of interaction with an international and multicultural scientific community and takes place in a collaborative and collegial team environment. 

Position Requirements

Essential Duties and Responsibilities

  • Perform a critical review of experimental data, provide recommended values, and combine these values with theoretical calculations to develop new ENDF evaluations. 
  • Develop applications that apply Machine Learning techniques to accelerate data analysis, compilation, and evaluation.     
  • Participate in and analyze experiments related to the ENDF database and/or perform theoretical calculations to complement nuclear reaction measurements supporting ENDF library improvements.   
  • Document and disseminate research results through publications and conduct research presentations at meetings and conferences. 

Required Knowledge, Skills, and Abilities

  • Bachelor’s and 8+ years, or Master’s and 6+ years, or Doctorate and 2+ years post PHD in experimental or theoretical low-energy nuclear physics or nuclear engineering or directly related  experience in a scientific or research role 
  • Demonstrated expertise in low energy reaction theory or experiment 
  • Solid publication record in peer-reviewed journals 
  • Programming in C/C++ and Python languages 
  • Clear and concise oral and written communication skills in order to prepare and deliver oral presentations and write articles for scientific journals 
  • Attention to detail 

Preferred Knowledge, Skills, and Abilities

  • Demonstrated expertise in one of the following areas: (a) low-energy nuclear reaction modeling, (b) R-matrix fitting of experimental data 
  • Experience with data science and scientific data management 
  • Familiarity with the Fortran programming language 
  • Experience with parallel computation on HPC 
  • Experience with Machine Learning algorithms applicable to physical sciences, such as Gaussian Process and Bayesian network approaches 

Environmental, Health & Safety Requirements:

  • The incumbent must be available to start by December 1, 2022 
  • Appointment level will be commensurate with experience and qualifications 
  • Initial 2-year term appointment subject to renewal contingent on performance and funding. 

Brookhaven Science Associates requires proof of a COVID-19 vaccination for all employees. Proof of full vaccination as recognized by the CDC and/or WHO, inclusive of the two-week waiting period, is required at the start of your employment.

Brookhaven National Laboratory (BNL) is an equal opportunity employer that values inclusion and diversity at our Lab. We are committed to ensuring that all qualified applicants receive consideration for employment and will not be discriminated against on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, age, status as a veteran, disability or any other federal, state or local protected class.

BNL takes affirmative action in support of its policy and to advance in employment individuals who are minorities, women, protected veterans, and individuals with disabilities. We ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.

*VEVRAA Federal Contractor

Brookhaven employees are subject to restrictions related to participation in Foreign Government Talent Recruitment Programs, as defined and detailed in United States Department of Energy Order 486.1A. You will be asked to disclose any such participation at the time of hire for review by Brookhaven. The full text of the Order may be found at: https://www.directives.doe.gov/directives-documents/400-series/0486.1-BOrder-a/@@images/file

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