AI-Driven Lab Automation Research Fellow in Life Sciences

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 Lab Automation for Life Sciences in the Biological, Environmental and Planetary Science Division, on-site in Upton, NY. The role focuses on advancing structural biology through AI-enabled crystallization methods and high-throughput workflows.

The ideal candidate has interests in biophysics, biochemistry, artificial intelligence, and machine learning; collaboration across science teams and preparation

Qualifications

• Ph.D. in computer science, bioinformatics, biophysics, applied mathematics, or related field.

Responsibilities

• Develop AI-enabled lab workflows for automated crystallization screening, optimization, and characterization.
• Design and deploy computer vision methods to detect and track crystal growth.
• Develop closed-loop optimization to recommend crystallization conditions based on experimental feedback.
• Integrate AI/ML models with lab automation systems (crystallization robotics, liquid handlers, imaging platforms).
• Build scalable data pipelines linking metadata, imaging data, and diffraction results for high-throughput analysis.
• Evaluate model performance using experimental metrics and support deployment into workflows.
• Collaborate with beamline scientists and lab staff; document methods and results; contribute to manuscripts and reports.
• Participate in interdisciplinary team science.

Skills

Machine learning

Computer vision

Image analysis

Python

Git

Data management

Interdisciplinary teamwork

Visualization

Education

Ph.D. in computer science or related field

Tools

Git

Job description

Brookhaven National Laboratory's NSLS-II seeks a Postdoctoral Research Associate for AI-driven Lab Automation for Life Sciences in the Biological, Environmental and Planetary Science Division, on-site in Upton, NY. The role focuses on advancing structural biology through AI-enabled crystallization methods and high-throughput workflows.

The ideal candidate has interests in biophysics, biochemistry, artificial intelligence, and machine learning; collaboration across science teams and preparation