Recruitment
Yamaha Corporation is seeking talented and motivated R&D engineers from around the world for its internship program in Japan.
Interns will be based at Yamaha’s Innovation Center, working closely with researchers and engineers as members of project teams. The program runs for approximately 12 weeks, with travel and accommodation expenses fully covered. This is a unique opportunity to gain hands-on experience and contribute to cutting-edge innovation at Yamaha.
Global Internship
Yamaha offers internship opportunities in its Research & Development and Product Development divisions, as well as a postdoctoral position.
Research & Development Internship
Outline
| Term | Approximately three months, Summer to Autumn 2027 |
|---|---|
| Division | Research & Development Division |
| Location | Hamamatsu / Yokohama, Japan |
| Requirement | Doctoral or Master's students in engineering, computer science, mathematics, or related fields. |
| Condition | Paid internship |
| Benefits | Interns will receive a stipend. We also provide: ・ Visa application support ・ Transportation cost assistance ・ Furnished accommodation |
| Application | Details will be provided when the application form opens on September 28, 2026. |
| Process | Application submission followed by an online interview |
| Due date | November 18, 2026 (AOE, UTC-12) |
Conditional Accompaniment Generation for Free-Tempo Musical Performance
Recent advances in deep learning have enabled high-quality music generation. However, generating accompaniment that follows a musician’s expressive, free-tempo performance in real time remains a challenging problem.
In this internship, you will develop a conditional accompaniment generation system guided by user performance. By using musical scores and/or reference audio as conditions, the system will generate accompaniment for a specific musical piece while adapting to the user’s timing and expression. The project will involve investigating low-latency music generation and performance-following techniques for human-AI ensemble performance.
Required:
- Experience with deep learning frameworks such as PyTorch
Preferred:
- Knowledge of music information retrieval (MIR), audio signal processing, or symbolic music processing
- Experience in music generation, score following, or real-time interactive music systems
Related Technology:
Development of Ultra-Low-Latency Music Source Separation
This project aims to develop core technologies for next-generation music production and musical experiences through ultra-low-latency music source separation. You will explore machine learning and audio signal processing techniques to achieve high-quality source separation with minimal processing delay. While the project addresses music source separation in general, a primary focus will be on extracting clean vocal signals by suppressing bleed, acoustic feedback, and other unwanted components. You will develop and evaluate source separation models, as well as analyze the trade-offs among latency, computational efficiency, and separation performance. In addition, based on these investigations, you will explore and propose novel approaches for real-time audio processing.
Required:
- Proficiency in Python programming
- Strong research background in machine learning and/or statistical analysis
Preferred:
- Knowledge of mixing process and audio effects
- Experience in audio signal processing
Related Technology:
Modeling Piano Key Force Inputs and Their Application to Inverse Dynamics Analysis of Piano Performance
Pianists generate a wide range of sounds through subtle variations in the force applied to the keyboard. Modeling these control inputs can contribute to improved instrument design, performance analysis, and feedback systems for piano education.
This project aims to estimate and model force inputs to piano keys using multimodal instrument and performer sensing, including force transducers, key and hammer motion sensing, and motion capture data. You will develop machine learning models that infer force-related control variables from sensor measurements and, where feasible, integrate these estimates into inverse dynamics analyses to better understand the biomechanics of piano performance.
This project aims to estimate and model force inputs to piano keys using multimodal instrument and performer sensing, including force transducers, key and hammer motion sensing, and motion capture data. You will develop machine learning models that infer force-related control variables from sensor measurements and, where feasible, integrate these estimates into inverse dynamics analyses to better understand the biomechanics of piano performance.
Required:
- Experience applying machine learning to sequential or time-series data
- Strong programming skills in Python and scientific computing
- Basic knowledge of piano performance and piano mechanics
Preferred:
- Experience with multimodal sensor data (motion capture, force sensors, IMUs, etc.)
- Experience with probabilistic modeling, state-space models, or physics-informed machine learning
- Experience with music performance science research, biomechanics (esp. dynamics / sensing devices), or human-computer interaction
Related Technology:
Gray-box Modeling for Audio Equipment and Musical Instruments
Audio equipment and musical instruments are complex systems in which mechanical vibration, acoustics, electric circuits, and performance inputs interact, often with significant nonlinear behavior. This internship focuses on gray-box modeling, which combines physical and mathematical prior knowledge with data-driven modeling techniques, such as machine learning-based system identification, parameter estimation, and inverse modeling. Target systems may include loudspeakers, amplifiers, wind instruments, and FM synthesizers. The project supports behavior prediction, control, and model-based design through accurate and interpretable modeling. Using experimental facilities and measured data from actual devices, you will develop and validate models for a deeper understanding and optimization of audio products and musical instruments.
Required:
- Experience in programming (Python, C/C++, or Matlab)
- Strong knowledge and practical experience in data-driven modeling
Preferred:
- Experience in physical modeling or system identification
- Knowledge of signal processing or acoustics
- Experience in playing musical instruments, or enthusiasm for audio products
Related Technology:
Enhancing Subjective Evaluation Models for Piano Sound Using Performance Data
We have developed subjective evaluation models for piano sound by collecting and systematically organizing the words pianists use to describe sound in interviews and performance evaluations. While these language-based models provide a solid foundation for capturing how pianists perceive and evaluate sound, certain aspects are difficult to fully represent through words alone. We therefore aim to further refine these models by incorporating additional performance data, such as MIDI recordings and video footage.
In this internship, you will use a multimodal measurement system to record piano performance data, including MIDI, video, and audio. You will then extract relevant features from the collected data, analyze performance characteristics and patterns, and compare the findings with the existing evaluation models. This will help identify aspects of piano performance that are captured in multimodal data but difficult to explain through language-based evaluation alone. The broader goal is to identify candidate features and develop hypotheses about piano performance and evaluation that can complement and extend our current models.
Required:
- Experience in time-series data analysis (Python, etc.)
- Experience in statistical analysis and modeling
- Interest in human perception and subjective evaluation
Preferred:
- Experience in MIDI, video, audio, and multimodal data analysis
- Knowledge of psychophysics, sensory evaluation, and cognitive science
- Knowledge of music information retrieval
Related Technology:
Product Development Internship
Outline
| Term | Approximately three months, Summer to Autumn 2027 |
|---|---|
| Division | Product Development Division |
| Location | Hamamatsu / Yokohama, Japan |
| Requirement | Master or Senior students in engineering, computer science, mathematics, or related fields. |
| Condition | Paid internship |
| Benefits | Interns will receive a stipend. We also provide: ・ Visa application support ・ Transportation cost assistance ・ Furnished accommodation |
| Application | Details will be provided when the application form opens on September 28, 2026. |
| Process | Application submission followed by an online interview |
| Due date | November 18, 2026 (AOE, UTC-12) |
Development of Digital Effects for Synthesizers & Keyboards
Even today, simulating analog effects in digital musical instruments and developing high-performance effect algorithms in the digital domain remain essential. During this internship, you will participate in development work that includes the following tasks, contributing to the improvement of the quality of future Yamaha products.
Prototyping VST3 Digital Effects for Synthesizers and Keyboards:
- understanding prototyping framework and development setup
- researching and designing digital audio effects algorithm
- realtime audio signal processing implementation as VST3 plugin
- sound quality verification and value proposition validation
Prototyping VST3 Digital Effects for Synthesizers and Keyboards:
- understanding prototyping framework and development setup
- researching and designing digital audio effects algorithm
- realtime audio signal processing implementation as VST3 plugin
- sound quality verification and value proposition validation
Required:
- Experience in designing DSP algorithm
- Experience in implementing DSP algorithm in C/C++
- Basic knowledge of digital audio
- Basic knowledge of audio effects and gears
- Basic knowledge of synthesizers
- Experience in music production software (DAW)
Preferred:
- Basic knowledge of electronic circuit design and/or acoustics
- Basic knowledge of music and musical instruments
- Experience in playing musical instruments
- Basic knowledge of machine learning
Related Technology:
Post Doctoral Position
The application
period for Post
Doctral Position has been closed.
Please await further announcements regarding our next call for applications.
| Position | Researcher for Audio Signal Processing and Music Informatics |
|---|---|
| Location | Hamamatsu/Yokohama, Japan |
| Description |
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| Qualifications |
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| Due Date | 15 September, 2024 (Messages via the contact form must be received no later than 11:59 pm on 15 September 2024, SST (UTC -11:00).) |
| How to Apply |
Please fill in the required fields in the application form as well as enter the
information below when applying.
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