Post-Doctoral Research Associate - Natural Language Processing - Brookhaven National Laboratory

The AI and Machine Learning Department at Brookhaven National Laboratory (BNL) invites exceptional candidates to apply for a post-doctoral research associate position with a focus on national security applications of natural language processing (NLP). This extremely fast-moving and competitive field has produced innovations with highly visible impact in industry, education, and public life – including the explosion of large language models (LLMs). BNL is engaged in numerous research efforts that employ NLP techniques for science and security applications and uses these applications to inform new foundational ML and NLP innovations.

The position provides unique access to world-class computing resources, such as the BNL Institutional Cluster and DOE leadership computing facilities, as well as collaboration opportunities with domain scientists and security experts. 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 from a variety of domain sciences and security areas.
• Formulate own high-quality research ideas and execute on them in collaboration with mentors in the department.
• Communicate research progress, challenges, and achievements, and engage within and beyond the department on new potential collaborations.

Required Knowledge, Skills, and Abilities:

• Ph.D. in computer science or a related field (e.g., engineering, applied mathematics, statistics) awarded within the last 5 years.
• Strong theoretical understanding and practical experience in deep learning-based machine learning and natural language processing.
• Strong research experience (e.g., evidenced by publication record).
• Excellent programming and computer science skills.
• Security clearance requirements: Must undergo and receive a favorable disposition in a preliminary background investigation (criminal, credit, prior employment, etc.); must be able to obtain and maintain a U.S. Department of Energy Q-level security clearance which requires that you: be a US citizen; have no felony convictions or other serious offenses; have an honorable discharge from military, and a good credit history.  Obtaining and maintaining a security clearance is a condition of employment.

Preferred Knowledge, Skills, and Abilities:

• Practical experience developing novel ML and NLP algorithms and models and 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 in Upton, NY

Compensation:

• Brookhaven National Laboratory is committed to providing fair, equitable and competitive compensation. The full salary range for this position is $70200 - $116200 / year. Salary offers will be commensurate with the final candidate’s qualification, education and experience and considered with the internal peer group.