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.

Position Description

The Machine Learning Group of the Computational Science Initiative (CSI) at Brookhaven
National Laboratory (BNL) invites exceptional candidates to apply for a post-doctoral research
associate position in machine learning (ML). This position offers a unique opportunity to conduct
both basic and applied research in concert with collaborators working on diverse scientific and
security problems of interest to BNL and the Department of Energy (DOE).

Topics of particular interest include: (i) novel development of deep learning ML models and
adaptation of existing ones for scientific and security applications; (ii) ML models for natural
language processing (NLP), including Large Language Models (LLMs) and multi-modal,
multi-task Foundation Models; and (iii) techniques supporting stakeholders and end-users of
applied ML methods, including uncertainty quantification (UQ), interpretability and explainability
(XAI), and visualization techniques.

The position provides access to world-class computing resources, such as the BNL Institutional
Cluster and DOE leadership computing facilities. Access to these platforms will allow computing
at scale, and together with access to unique data sources, will ensure that the successful
candidate has the necessary resources to solve challenging DOE problems of interest. The
successful candidate will join a growing research group with diverse expertise and projects
spanning the full breadth of BNL’s and the DOE’s missions.

 This post-doc position presents a unique chance to conduct interdisciplinary collaborative research in BNL
programs with a highly competitive salary.

Essential Duties and Responsibilities:

● Conduct research in ML and NLP for various problems relating to scientific discovery,
workflow acceleration, and national security.
● Implement, adapt, and evaluate ML and NLP algorithms for scientific and security
applications.
● Work in interdisciplinary collaborations with subject matter experts on various aspects of
scientific data generation and processing, and methods evaluation.
● Formulate own high-quality research ideas and directions in collaboration with mentors
in the group.
● Communicate research progress, challenges and achievements, and engage within and
beyond the group on new potential collaborations.

Position Requirements

Required Knowledge, Skills, and Abilities:

● Ph.D. in computer science or a related field (e.g., engineering, applied mathematics,
statistics, physics) awarded within the last 5 years.
● Strong theoretical understanding and practical experience in deep learning-based
machine learning or natural language processing.
● Strong research experience (e.g. evidenced by publication record).
● Excellent programming and computer science skills.
● Ability to obtain and maintain security clearance, which requires U.S. Citizenship.
● BNL cannot sponsor US work authorization/visa for this position.

Preferred Knowledge, Skills, and Abilities:

● Practical experience developing novel ML or NLP algorithms and models, and/or
applying such models to scientific or security problems.
● Experience working in multidisciplinary collaborations.

OTHER INFORMATION:

• • Initial 2-year term appointment subject to renewal contingent on performance and funding
  • BNL policy requires that after obtaining a PhD, eligible candidates for research associate appointments may not exceed a combined total of 5 years of relevant work experience as a post-doc and/or in an R&D position, excluding time associated with family planning, military service, illness or other life-changing events
  • This is a fully onsite position located at BNL

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