Postdoctoral Research Associate - AI-driven Lab Automation for 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 National Synchrotron Light Source II (NSLS‑II) seeks a Postdoctoral Research Associate for AI‑driven Lab Automation for Life Sciences for an on‑site position in the Biological, Environmental and Planetary Science Division.

The aim of the position is to advance the frontiers of structural biology by developing AI‑driven methods for improving crystal quality at scale. This role will enable new discoveries in life sciences and bio‑technology relying on structural biology methods.

The ideal candidate would have a background and an interest in biophysics, biochemistry, artificial intelligence and machine learning.

Join a team of scientists at the leading macromolecular crystallography beamlines, the crystallization laboratory, the computing center and contribute to science projects at the interface between AI method development and large‑scale research facilities.

Essential Duties and Responsibilities

• Develop and implement AI‑enabled laboratory‑workflows for automated crystallization screening, optimization, and characterization integrating imaging, experimental metadata, and diffraction outcomes.
• Design and deploy computer vision methods to detect and track crystal growth.
• Develop closed‑loop optimization approaches to recommend crystallization conditions and harvesting strategies based on experimental feedback.
• Develop, train and integrate AI/ML models with laboratory automation systems, including crystallization robotics, liquid handlers, and imaging platforms.
• Build scalable data pipelines linking experimental metadata, imaging data, and diffraction results for high‑throughput analysis.
• Evaluate model performance using experimental metrics and support deployment into user‑facing workflows.
• Collaborate with synchrotron beamline scientists and laboratory and crystallization staff.
• Document methods and results, contribute to manuscripts and reports, and present the work at conferences.
• Participate in interdisciplinary team science.

Required Knowledge, Skills, and Abilities

• Ph.D. in computer science, bioinformatics, biophysics, applied mathematics, or related field.
• Expertise in machine learning, computer vision, or image analysis.
• Experience with data management and databases.
• Experience working with Python, scientific software development, version control and collaborative code development, such as Git.
• Ability to work in interdisciplinary teams.
• Strong publication record.

Preferred Knowledge, Skills, and Abilities

• Experience with machine learning frameworks (PyTorch, TensorFlow, etc.) and integrating ML in a research lab facility.
• Familiarity with lab automation or robotics.
• Experience with black‑box optimization, including active learning or Bayesian optimization.
• Experience with imaging, time‑series or high‑dimensional data.
• Exposure to crystallography or structural biology.
• Experience with multimodal datasets and developing reproducible workflows.
• Familiarity with experiment tracking and metadata capture.

Additional Information

• This position is a 2‑year term, with the possibility of renewal contingent on performance and funding availability.
• Brookhaven Laboratory is committed to providing fair, equitable and competitive compensation. The full salary range for this position is $71,900-$119,000 per year; compensation will be commensurate with the final candidate's qualifications, education and experience.
• Candidates must have completed all degree requirements by the commencement of employment.
• 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.

Equal Opportunity Employer

Brookhaven Science Associates is an Equal Employment Opportunity Employer. We consider all qualified applicants without regard to any characteristic protected by law. Reasonable accommodations are provided for individuals with disabilities.