Student Research Assistant - Autonomous Imaging
About The Position
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.
Requirements
- Must be a college or graduate student enrolled in computer science related or STEM field.
- Strong skills in deep learning and programming with Python, and computer vision related technique.
- Ability to communicate effectively, both verbally and in writing.
Nice To Haves
- 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.
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.
Benefits
- Comprehensive employee benefits program
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What This Job Offers
Job Type
Part-time
Career Level
Entry Level
Education Level
No Education Listed
