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Working Student – Deep Learning for Spatial Audio(m/w/x)
Exploring Ambisonics coding and developing DNN-based upmixing methods for spatial audio. Python and PyTorch experience required. Flexible working hours and days.
Requirements
- Bachelor's degree in science or engineering
- Currently enrolled in related master's degree
- Hands-on experience with Python
- Hands-on experience with PyTorch
- Completed small deep-learning project
- Ability to work independently
- Delivery of reliable results
- Excellent German skills
- Solid English skills
Tasks
- Explore Ambisonics coding in 3GPP IVAS codec
- Develop methods for rendering Ambisonics signals
- Implement methods for upmixing Ambisonics signals
- Test rendering and upmixing methods
- Reproduce DNN-based upmixing techniques
- Improve DNN-based coding of Ambisonics signals
Education
- Currently in higher educationOR
- Bachelor's degree
Languages
- German – Native
- English – Business Fluent
Tools & Technologies
- Python
- PyTorch
Benefits
Flexible Working
- Flexible working hours
- Individual start date agreement
- Individual weekly working hours agreement
- Flexible working days
Informal Culture
- Open and friendly working atmosphere
Purpose-Driven Work
- Valued ideas
- Application-oriented research
Diverse Work
- Inspiring and challenging tasks
Career Advancement
- Opportunities for full-time employment after studies
Family Support
- Opportunities for part-time employment after studies
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Working Student – Deep Learning for Spatial Audio(m/w/x)
Exploring Ambisonics coding and developing DNN-based upmixing methods for spatial audio. Python and PyTorch experience required. Flexible working hours and days.
Requirements
- Bachelor's degree in science or engineering
- Currently enrolled in related master's degree
- Hands-on experience with Python
- Hands-on experience with PyTorch
- Completed small deep-learning project
- Ability to work independently
- Delivery of reliable results
- Excellent German skills
- Solid English skills
Tasks
- Explore Ambisonics coding in 3GPP IVAS codec
- Develop methods for rendering Ambisonics signals
- Implement methods for upmixing Ambisonics signals
- Test rendering and upmixing methods
- Reproduce DNN-based upmixing techniques
- Improve DNN-based coding of Ambisonics signals
Education
- Currently in higher educationOR
- Bachelor's degree
Languages
- German – Native
- English – Business Fluent
Tools & Technologies
- Python
- PyTorch
Benefits
Flexible Working
- Flexible working hours
- Individual start date agreement
- Individual weekly working hours agreement
- Flexible working days
Informal Culture
- Open and friendly working atmosphere
Purpose-Driven Work
- Valued ideas
- Application-oriented research
Diverse Work
- Inspiring and challenging tasks
Career Advancement
- Opportunities for full-time employment after studies
Family Support
- Opportunities for part-time employment after studies
Like this job?
BetaYour Career Agent finds similar jobs for you every day.
About the Company
Fraunhofer-Institut für Integrierte Schaltungen IIS
Industry
Research
Description
Das Unternehmen ist eines der weltweit führenden Institute für anwendungsorientierte Forschung in den Bereichen Künstliche Intelligenz, Mikroelektronik und Datenerfassung.
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