PRN

Saint Louis University • Saint Louis, MO

2d • Onsite

About The Position

The GXR Lab is seeking a highly motivated and multidisciplinary Graduate Research Assistant to join our team in developing next-generation eXtended Reality (XR) systems. This role bridges the gap between physical mechanics, computer vision, and immersive digital environments. The ideal candidate will leverage expertise in physics-based simulation, computer vision, and mechanical design to support our current research portfolio—spanning AI-driven adaptive VR sports training (e.g., table tennis, golf), neurocognitive behavioral assessments, and video see-through AR for vision Therapy. This position is being funded by a grant and/or designated source and therefore continuation in the position is contingent upon funding availability.

Requirements

• B.S. in ME or CS and pursuing a PhD in ME, CS, or a closely related field.
• Proficiency in Python, OpenCV, C++, MATLAB, and Finite Element Analysis (FEA) tools (such as Abaqus or SolidWorks simulation modules).
• Experience with Computer-Aided Design (SolidWorks, Creo, Autodesk Fusion) and hands-on rapid prototyping techniques (FDM/SLA 3D printing).
• Strong alignment with research exploring data-driven physical simulation, soft-bodied interactions, or biomimetic system mechanics.

Responsibilities

• Develop and validate real-time, physics-based simulations and finite element models to refine biomechanical movement tracking and optimize training transfer from virtual to real-world environments.
• Implement OpenCV and data-driven pipelines to monitor, track, and analyze user skeletal information, attention data, and reaction times within controlled virtual environments.
• Design, 3D print (FDM/SLA), and rapidly prototype passive haptic devices, wearable sensor housings, and intuitive mechanical control interfaces to bridge the gap between physical mastery and virtual interaction models.
• Partner with domain-specific researchers to construct adaptive AI-coaching platforms, ensuring accurate physical feedback loops for tasks requiring high motor dexterity and precision.

What This Job Offers

• Job Type: Part-time
• Career Level: Entry Level