NIWC - Graduate Student - Shipboard Scene Understanding Detection and Identification of Objects; SSU

NIWC - Graduate Student - Shipboard Scene Understanding Detection and Identification of Objects (SSU

This role is offered through San Diego State University.

$22.39 per hour and is non‑negotiable. The base pay range is $22.39/hr - $22.39/hr
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Graduate level 1 or above (pursuing a Master’s or PhD in Computer Science, Electrical Engineering, Applied Mathematics, Data Science, or a related major). Under direction, incumbents must have coursework in the discipline requested on the task order Statement of Work. This Statement of Work covers the need for graduate student services to provide technical and/or analytical support in computer science and electrical engineering for a computer vision team, specifically for the Shipboard Scene Understanding Detection and Identification of Objects (SSU O) project.

The purpose of this project is to develop scene understanding from 3‑D scans of ships by applying machine learning/computer vision techniques. Additionally, the hired student will assist in corrosion/surface defect detection, object tracking of moving targets, and other machine learning‑based tasks. The computer vision team develops solutions for a range of vision tasks via machine learning and deep learning algorithms.

The SSU O project aims to identify various objects of interest from shipboard 3‑D scans by training computer vision algorithms to detect, localize, and classify objects.

• Develop and maintain computer programs using Python and other programming languages to support computer vision and machine learning projects.
• Research, develop, and improve machine learning workflows and applications.
• Utilize neural networks and deep learning for object classification, detection, segmentation, optical character recognition, and text localization.
• Complete data‑labeling tasks for machine learning models, including file and data format conversions; annotate data, including text transcription, classification, and object detection in 2‑D, 3‑D, and other data types.
• Conduct research, write reports/publish conference papers, and create demonstrations/visualizations to support the understanding of the algorithms and the data.
• Prepare monthly status reports and bi‑weekly time reports in accordance with the basic contract CORL.

• Graduate Level 1 or above: pursuing a Master’s or PhD in Computer Science, Electrical Engineering, Applied Mathematics, Data Science, or a related major.
• Functional knowledge of Python and common libraries (comfortable writing functions, classes, loops, etc. and using Anaconda or Python virtual environments).
• Proven oral and written communication skills and documentation habits.

• Familiarity with Computer Vision or Machine Learning frameworks (e.g., PyTorch and/or Tensor Flow).
• Familiarity with neural networks and deep learning.
• Experience with object classification, detection, segmentation, optical character recognition, or text localization.
• Experience with LiDAR and point clouds or other 3‑D work.
• Comfortable working in a Linux environment.
• Experience with GIT/source control.

• This position may require the employee to obtain and maintain a DoD security clearance.
• Due to DoD regulations, only U.S. citizens may qualify.
• This is a student position limited to 20 hours per week.
• Possible travel to conferences to present research papers and results.
• The student will work remotely most of the time and occasionally on‑site.
• This position will remain open until filled.
• Candidates must reside in California and live within a commutable distance from SDSU at the time of hire.
• Job offer is contingent upon satisfactory clearance based on background check results (including a criminal record check).

San Diego State University Research Foundation is an equal‑opportunity employer. Consistent with California law and federal civil‑rights laws, SDSU Research Foundation provides equal opportunity in employment without unlawful discrimination or preferential treatment based on race, sex, color, ethnicity, national origin, or any other categories protected by federal or state law. Employment decisions are based on an…