Postdoctoral Research Associate - AI-driven Structure Analysis for Biological Discoveries

Brookhaven Science Associates, LLC; Upton (NY); On-site; USD 71,900 - 119,000; Full time

Job summary

Brookhaven National Laboratory’s NSLS-II seeks an on-site Postdoctoral Research Associate in the Biological, Environmental and Planetary Science Division to develop AI-driven methods for interpreting complex crystallographic data.

The position focuses on advancing discoveries in protein function, ligand binding, and dynamics through scalable computational analysis.

Qualifications

• PhD in computer science, computational biology, physics, or a related field.
• Expertise in machine learning or AI.
• Experience with molecular dynamics simulations and computational workflows.
• Ability to work effectively in teams.
• Demonstrated publications in the field.

Responsibilities

• Develop AI-driven methods for interpreting crystallographic data to advance discoveries in protein function, ligand binding, and dynamics.
• Build and curate training datasets linking electron density maps and structural models.
• Design and evaluate AI models for 3D structural data.
• Develop workflows for time-resolved crystallography data processing.
• Integrate molecular dynamics with AI/ML methods.
• Benchmark protein foundation model performance and integrate them with experimental analysis pipelines.
• Work collaboratively with domain scientists, beamline scientists, and computational scientists.
• Document methods and results.

Skills

Machine learning

Molecular dynamics

Team collaboration

Publications

AI/ML pipelines

Education

PhD in CS/CompBio/Physics

Tools

HPC

Workflow orchestration

Surrogate modeling

Job description

Postdoctoral Research Associate – AI‑driven Structure Analysis for Biological Discoveries

Brookhaven National Laboratory’s National Synchrotron Light Source II (NSLS‑II) seeks an on‑site Postdoctoral Research Associate in the Biological, Environmental and Planetary Science Division. The role focuses on developing AI‑driven methods for interpreting complex crystallographic data to advance discoveries in protein function, ligand binding, and dynamics through scalable computational analysis.

Responsibilities

• Develop workflows to improve models and maps to optimize likelihood for ligand placement.
• Develop AI/ML methods for automated interpretation of crystallography data for optimal ligand placement.
• Build and curate training datasets linking electron density maps and structural models.
• Design and evaluate AI models for 3D structural data.
• Develop workflows for time‑resolved crystallography data processing.
• Integrate molecular dynamics with AI/ML methods.
• Benchmark protein foundation model performance and integrate them with experimental analysis pipelines.
• Work collaboratively with domain scientists, beamline scientists, and computational scientists.
• Document methods and results.

Required Knowledge, Skills, and Abilities

• Ph.D. in computer science, computational biology, physics, or a related field.
• Expertise in machine learning or AI.
• Experience with molecular dynamics simulations and computational workflows.
• Ability to work effectively in teams.
• Demonstrated track record of publications in the field.

Preferred Knowledge, Skills, and Abilities

• Experience with structural biology, 3D ML, and biology‑specific AI models.
• Experience with HPC and surrogate modeling.
• Familiarity with workflow orchestration tools.

Additional Information

The position is for a 2‑year term, with the possibility of renewal contingent on performance and funding availability.

The full salary range is $71,900 – $119,000 per year. Salary offers will be commensurate with the final candidate’s qualifications, education, and experience and considered within the internal peer group.

Applicants must have completed all degree requirements 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 five 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.

Brookhaven National Laboratory requires all non‑badged personnel, including visitors, to produce a REAL‑ID or REAL‑ID compliant document for access to the laboratory. The selected candidate must be able to obtain and maintain a DOE Uncleared Personal Identity Verification (UPIV) credential.

Equal Opportunity

Brookhaven Science Associates is an Equal Employment Opportunity Employer. We consider all qualified applicants without regard to any characteristic protected by law.