The Artificial Intelligence Learning department of the Computing and Data Sciences (CDS) directorate 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) development of novel machine learning models and adaptation of existing approaches for scientific applications; (ii) Large Language Models (LLMs) and multimodal foundation models for cross-domain reasoning and knowledge integration; (iii) continuous learning and model adaptation in dynamic and evolving environments; and (iv) Agentic AI systems for autonomous discovery, planning, and decision-making.

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.

Essential Duties and Responsibilities:

• Conduct research in ML, multimodal foundation models, continuous learning, and agentic AI systems for various problems relating to autonomous scientific discovery.

• Work in interdisciplinary collaborations with subject matter experts on various aspects of scientific data generation and processing and methods evaluation.

• Formulate high-quality research ideas and directions in collaboration with mentors in the department.

• Communicate research progress, challenges, and achievements, and engage within and beyond the department 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)

• Strong theoretical understanding and practical experience in machine learning, multimodal foundation models, continuous learning, and agentic AI techniques

• Demonstrated publication record in machine learning field

• Excellent programming and computer science skills

Preferred Knowledge, Skills, And Abilities:

• Practical experience developing novel ML, multimodal foundation models, continuous learning systems, or agentic AI models.

• Experience with state-of-the-art multimodal foundation models and agentic AI frameworks

• Experience in large-scale deep learning systems, large multimodal foundation model training and/or finetuning, and continuous learning pipelines.

• Experience in multi-modality data analysis (e.g., image, video, text).

• Experience working in multidisciplinary collaborations.

Other Information:

• Initial 2-year term appointment subject to renewal contingent on performance and funding

• Candidates must have received a Ph.D. by the commencement of employment.

• 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

Brookhaven National Laboratory is committed to providing fair, equitable and competitive compensation. The full salary range for this position is $74050 - $122550 / year. Salary offers will be commensurate with the final candidate6s qualification, education and experience and considered with the internal peer group.

Brookhaven National Laboratory is committed to employee success and we believe that a comprehensive employee benefits program is an important and meaningful part of the compensation employees receive. Review more information at