Student Research Assistant - Autonomous Imaging

The National Synchrotron Light Source II (NSLS-II) at Brookhaven National Laboratory seeks a Student Researcher to support the X-AutoMap project, an initiative focused on autonomous hard X-ray spectromicroscopy. The project utilizes the Hard X-ray Nanoprobe (HXN) capabilities, which provide ultra-high spatial resolution scanning imaging for multimodal elemental (XRF) and chemical (XANES) analysis. The X-AutoMap framework is currently under development, with the goal of integrating Computer Vision models and AI-driven segmentation to enable active feedback control for characterizing chemically heterogeneous systems, such as rare earth minerals.
As a Student Researcher for X-AutoMap, the successful candidate will work closely with a team including beamline scientists to implement and validate AI-driven scanning workflows. The Student Researcher will assist in adapting vision models for real-time feature detection and integrating them into the beamline control system (Bluesky), helping to transform the microscope into an intelligent agent capable of efficient, autonomous discovery.

Essential Duties and Responsibilities:

• Implement Vision Models: Adapt Deep Vision Models (e.g., Vision Transformers) to identify mineral features.

• Develop Autonomous Framework: Build a "Smart" scanning framework for autonomous microscope operation.

• Create Active Feedback: Implement logic to trigger high-resolution XRF and XANES scans based on real-time data.

• Validate Performance: Test the autonomous method on rare earth mineral samples to ensure efficient characterization.

POSITION REQUIREMENTS

Required Knowledge, Skills, and Abilities:

• Must be a college or graduate student enrolled in computer science related or STEM field.

• You need strong skills in deep learning and programming with Python, and computer vision related technique

• You communicate effectively, both verbally and in writing.

Preferred Knowledge, Skills, and Abilities:

• Demonstrated track record in computer vision or deep learning through open-source project or GitHub.

• Experience X-ray microscopy data analysis.

• Knowledge of autonomous experimentation or control systems.

• Ability to work effectively in a collaborative team to tackle challenging problems, such as automating complex scientific experiments

Other Information:

• Temporary/Part-time schedule of 16 hours per week.

• This position is not eligible for visa sponsorship

Brookhaven National Laboratory is committed to providing fair, equitable and competitive compensation. The salary range for this position is $17.00 - $31.75 / hour. Salary offers will be commensurate with the final candidate’s qualification, education and experience and considered with the internal peer group. 

Brookhaven National Laboratory is committed to employee success and we believe that a comprehensive employee benefits program is an important and meaningful part of the compensation employees receive. Review more information at BNL | Benefits Program

Brookhaven National Laboratory requires all non-badged personnel including visitors to produce a REAL-ID or REAL-ID compliant documentation to access Brookhaven National Laboratory – view more information at www.bnl.gov/real-id.  This is due to nationwide identification requirements for federal site access as required by the federal REAL ID Act.  Those not in possession of a REAL ID-compliant document will not be permitted to access the site which includes access to the Laboratory for interviews. 

About Us

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 Uni