Postdoc: AI for Protein Structure & Crystallography

Brookhaven National Laboratory; Upton (NY); On-site; USD 71,900 - 119,000; Full time

Job summary

Brookhaven National Laboratory's NSLS-II seeks a Postdoctoral Research Associate for AI-driven Structure Analysis for Biological Discoveries, on-site in the Biological, Environmental and Planetary Science Division.

Advance structural biology by developing AI-driven methods for crystallographic data interpretation, enabling discoveries in protein function, ligand binding, and dynamics through scalable computational analysis.

Qualifications

• Ph.D. in computer science, computational biology, physics, or related field.
• Expertise in machine learning or AI.
• Experience with molecular dynamics simulations and computational workflows.
• Ability to work in teams; demonstrated publications in the field.

Responsibilities

• Develop AI-driven workflows to improve models and maps for ligand placement.
• Develop AI/ML methods for automated interpretation of crystallography data for 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 with analysis pipelines.
• Collaborate with domain scientists, beamline scientists, and computational scientists.
• Document methods and results.

Skills

Machine learning/AI

Team collaboration

Publications track record

Education

PhD in computer science

PhD in computational biology

PhD in physics

Job description

Brookhaven National Laboratory's NSLS-II seeks a Postdoctoral Research Associate for AI-driven Structure Analysis for Biological Discoveries, on-site in the Biological, Environmental and Planetary Science Division.

Advance structural biology by developing AI-driven methods for crystallographic data interpretation, enabling discoveries in protein function, ligand binding, and dynamics through scalable computational analysis.