PostDoc – Machine Learning

Brookhaven Science Associates, LLC; Upton (NY); On-site; USD 74,050 - 122,550; Full time

Job summary

Brookhaven Science Associates, LLC is offering a post-doctoral research associate position in machine learning in Upton, NY. This role focuses on developing innovative machine learning models and working on interdisciplinary research with diverse scientific applications.

Qualified candidates will hold a Ph.D. in computer science or related fields, with strong programming skills and a demonstrated publication record in machine learning. The successful candidate will benefit from world-class computing resources and a collaborative research environment.

Qualifications

• Strong theoretical understanding and practical experience in machine learning.
• Demonstrated publication record in machine learning field.
• Experience in multi-modality data analysis (e.g., image, video, text).

Responsibilities

• Conduct research in ML and autonomous scientific discovery.
• Work in interdisciplinary collaborations on scientific data generation.
• Communicate research progress and engage in collaborations.

Skills

Machine learning

Multimodal foundation models

Continuous learning

Programming skills

Education

Ph.D. in computer science or related field

Job description

Job Summary

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;
• (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.

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.

Required Qualifications

• 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 Qualifications

• 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 Ph.D., 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 requires all non‑badged personnel including visitors to produce a REAL‑ID or REAL‑ID compliant documentation to access Brookhaven National Laboratory – details available at www.bnl.gov/real-id.

Salary

The full salary range for this position is $74,050 - $122,550 per year. Salary offers will be commensurate with the final candidate’s qualification, education and experience and considered with the internal peer group.

Equal Opportunity Employer

Brookhaven Science Associates is an Equal Employment Opportunity Employer – Vets/Disabled. We consider all qualified applicants without regard to any characteristic protected by law. All qualified individuals are encouraged to apply. 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.