AR Tech Internship: Industrial Part Detection & Tracking

CSEM; Alpnach; Vor Ort; CHF 17.000 - 28.000; Vollzeit

Zusammenfassung

CSEM's Predictive Analytics group invites a student intern to develop an augmented reality system for detecting and identifying industrial parts on the shop floor. The project blends computer vision, object tracking, and 3D visualization to present part information directly through AR.

The work involves defining requirements, evaluating AR hardware, collecting and annotating data, and building a proof-of-concept demonstrator.

Qualifikationen

• Pursuing a Bachelor's or Master's in a relevant field.
• Strong Python programming skills.
• Experience in computer vision tasks such as detection/tracking.
• Familiarity with AR development frameworks.
• Interest in hands-on industrial experimentation and user-centered systems.
• Good English communication; German is a plus.

Aufgaben

• Define system requirements and representative industrial use cases with the project team.
• Test various AR hardware and libraries.
• Set up and document an experimental environment with industrial parts and shop-floor conditions.
• Acquire and annotate image or video data for part detection, tracking, and identification.
• Develop and evaluate computer-vision methods for detecting industrial parts.
• Design an AR interface that visualizes part information clearly.
• Assess robustness under changing viewpoints, lighting, occlusion, and backgrounds.
• Develop a proof-of-concept demonstrator and validate it through realistic experiments.
• Document implementation and results; communicate findings to the Predictive Analytics team.
• Assist the team with various tasks.

Kenntnisse

Python programming

Computer vision

AR concepts

3D graphics & visualization

ML/DL frameworks

Git & Linux

Team collaboration

English communication

Ausbildung

Bachelor's or Master's in CS/DS/EE/Robotics/ME/Microengineering

Jobbeschreibung

CSEM's Predictive Analytics group invites a student intern to develop an augmented reality system for detecting and identifying industrial parts on the shop floor. The project blends computer vision, object tracking, and 3D visualization to present part information directly through AR.

The work involves defining requirements, evaluating AR hardware, collecting and annotating data, and building a proof-of-concept demonstrator.