Brookhaven National Laboratory’s National Synchrotron Light Source II (NSLS-II) seeks a Postdoctoral Research Associate for AI-driven Structure Analysis for Biological Discoveries for an on-site position in the Biological, Environmental and Planetary Science Division.

Advance the frontiers of structural biology by developing AI-driven methods for interpreting complex crystallographic data. This role will enable new discoveries in protein function, ligand binding, and dynamics through scalable computational analysis.

The ideal candidate would have a background and an interest in Computational Biology, Artificial Intelligence and Machine Learning.

Join a team of scientists at the leading macromolecular crystallography beamlines and at 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 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 related field.

• Expertise in machine learning or AI.

• Experience with molecular dynamics simulations and computational workflows.

• Ability to work in teams.

• Demonstrated track record of publications in the field.

• Demonstrated commitment to working safely, acting ethically, and taking personal accountability for operational excellence.

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:

• 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 / year. You will be placed at the level and salary commensurate with your experience. Salary offers will be commensurate with the final candidate’s qualification, education and experience and considered with the internal peer group
• Candidates must have completed all degree requirements by the commencement of employment
• BNL policy requires that after obtaining a PhD, 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

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Brookhaven National Laboratory (www.bnl.gov) delivers discovery science and transformative technology to power and secure the nation’s future. Brookhaven Lab is a multidisciplinary laboratory with seven Nobel Prize-winning discoveries, 37 R&D 100 Awards, and more than 70 years of pioneering research. The Lab is primarily supported by the U.S. Department of Energy’s (DOE) Office of Science. Brookhaven Science Associates (BSA) operates and manages the Laboratory for DOE. BSA is a partnership between Battelle and The Research Foundation for the State University of New York on behalf of Stony Brook University. BSA salutes our veterans and active military members with careers that leverage the skills and unique experience they gained while serving our country, learn more at BNL | Opportunities for Veterans at Brookhaven National Laboratory.

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