Research Intern, XR Audio

Descripción del puesto:

The Immersive Audio Standardization (IAS) team consists of experienced members contributing to international standards in immersive audio coding and rendering, such as 3GPP IVAS and MPEG-I immersive audio. The team focuses on creating new immersive audio technologies and pushing them to global standards. We currently have an exciting internship opportunity related to audio for extended reality (XR).

* Explore and evaluate APIs of current XR devices (Apple ARKit, Google ARCore) to understand their capabilities for spatial sensing and scene understanding.
* Obtain room-scanning and spatial-mapping information from XR device APIs and associated SDKs.
* Provide the captured room-scanning data to an algorithm designed to analyze acoustic properties of the environment.
* Integrate the resulting acoustic analysis with an AR-capable renderer that will render spatial audio in real time.
* Do prototype development on platforms such as Unity & C#, Swift for iOS/VisionOS, Qt/QML 3D or other XR-specific frameworks

Requerimientos del candidato/a:

* B.Sc. or comparable level in a relevant field such as computer science, XR development, game programming, computer graphics, or 3D modeling.
* Good collaboration and communication skills (English mandatory), along with the ability to explore new APIs, understand technical documentation, and solve challenging problems independently.
* Active student status
It would be nice if you also had:
* Experience working with XR device APIs or SDKs (e.g., spatial sensing, room scanning, scene understanding).
* Familiarity with Unity & C#, Swift for iOS/visionOS, Qt/QML 3D, or other XR-focused development frameworks.
* Experience with 3D data formats such as meshes, polygons, point clouds, USD, or related processing libraries (e.g., trimesh).
* Interest or experience in audio, music or acoustics, especially in contexts where visual and spatial audio rendering interact.
* Hands-on experience with 3D tools or pipelines, for example Blender (preferred), Maya, 3ds Max, or spatial-data processing workflows