Master Thesis, Sound Experiences Lab

Stellenbeschreibung:

Join the leader in entertainment innovation and help us design the future. At Dolby, science meets art, and high tech means more than computer code. As a member of the Dolby team, you’ll see and hear the results of your work everywhere, from movie theaters to smartphones. We continue to revolutionize how people create, deliver, and enjoy entertainment worldwide. To do that, we need the absolute best talent. We’re big enough to give you all the resources you need, and small enough so you can make a real difference and earn recognition for your work. We offer a collegial culture, challenging projects, and excellent compensation and benefits, not to mention a Flex Work approach that is truly flexible to support where, when, and how you do your best work.

Summary

Dolby’s Sound Experiences Lab in Nürnberg specializes in audio codecs and their sonic quality evaluation. We apply modern deep learning techniques to advance the state of the art in audio coding performance and develop systems to evaluate audio quality. We are passionate about exploring and building new possibilities enabled by emerging technologies.

This thesis project will focus on exploring and developing a novel approach for predicting audio quality by leveraging latest tools and methodologies.

Responsibilities

  • Conduct a literature review on related research.
  • Reproduce and extend existing research approaches.
  • Propose and prototype new ideas.
  • Assist in the collection and processing of datasets.
  • Implement, train, and evaluate machine learning models.

Requirements

  • Currently enrolled in a Master’s program in Computer Science, Machine Learning, Statistics, Electrical Engineering, or a related technical field.
  • Strong knowledge of machine learning and deep learning concepts.
  • Familiarity with one or more neural network frameworks (e.g., TensorFlow, PyTorch).
  • Genuine interest in audio and/or media, with awareness of AI-related developments and trends.

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Stelleninformationen

  • Veröffentlichungsdatum:

    15 Apr 2026
  • Standort:

    Nürnberg
  • Typ:

    Vollzeit
  • Arbeitsmodell:

    Vor Ort
  • Kategorie:

  • Erfahrung:

    2+ years
  • Arbeitsverhältnis:

    Angestellt

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