Realtime DSP/ML Intern - Efficient Audio Modeling

World Aquatics; France; Sur place; EUR 12 000 - 17 000; Plein temps

Résumé du poste

The internship in the DSP-Machine Learning team within our R&D department at Arturia in Grenoble (Montbonnot-Saint-Martin) offers a 6-month hands-on opportunity to adapt trained PyTorch models for real-time CPU usage and embed them into audio plug-ins.

We seek a PhD student or final-year master/engineering student with solid DSP and ML knowledge, strong Python and PyTorch skills, and an interest in research and English communication. Public transport access is available.

Qualifications

• PhD student or master/final-year engineering student.
• Solid knowledge in DSP and ML.
• Proficiency in Python and ML frameworks (notably PyTorch).

Responsabilités

• Implement the SPTMod architecture in C++ using the Arturia framework.
• Conduct a bibliography on model compression (pruning, knowledge distillation, NAS).
• Apply techniques to concrete cases of effect modeling and compare them.
• Evaluate and optimize audio fidelity and real-time viability of modeling.
• Optimize the final C++ implementation.

Connaissances

DSP

Machine Learning

Python

PyTorch

C++

Audio DSP

Communication

Formation

PhD student, Master student or final-year engineering student

Outils

PyTorch

Python

Arturia framework

C++

Description du poste

The internship in the DSP-Machine Learning team within our R&D department at Arturia in Grenoble (Montbonnot-Saint-Martin) offers a 6-month hands-on opportunity to adapt trained PyTorch models for real-time CPU usage and embed them into audio plug-ins.

We seek a PhD student or final-year master/engineering student with solid DSP and ML knowledge, strong Python and PyTorch skills, and an interest in research and English communication. Public transport access is available.