Overview - Research and Development of Deep Learning Methods for Music and Dance

Researcher (Proponent) Natalia Polouliakh
Project Research and Development of Deep Learning Methods for Music and Dance
Expected Results Research and Development Leading to Service or Papers
Job description
(not limited to this list)

This project aims to research and develop deep learning models that generate music from dance and movement, revolutionizing fields such as creativity, health, and entertainment. It also presents a valuable opportunity to collaborate with renowned artists. Our current team is led by Natalia Polouliakh, Taketo Akama, Shun Minamikawa, other Sony employees, and several research assistant students. At Sony's research lab, where research spans diverse fields, each member is encouraged to conceive new concepts and engage in research, development, and the launch of new services.

We are seeking individuals who excel in modeling, have confidence in programming for model development, are passionate about this field, or wish to gain experience in a corporate research lab. If interested, please feel free to apply as soon as possible. When applying, please include details about your affiliation, skills and experience, published papers (if any), availability period, weekly working hours, and preferred working style (in-person in Tokyo or remote within Japan).

Required Skills

Preferred, but not mandatory, skills and experiences include:

  • Experience with programming languages such as Python.
  • Knowledge of deep learning, signal processing, and machine learning techniques.
  • Experience in information processing and programming related to music, audio, or dance.
Language requirement Japanese, English, or Russian
Duration

We prefer a commitment of more than three months, with a minimum of 10 hours per week. Working hours are relatively flexible, and you can adjust your working days and times each week to fit your schedule.

Place The workplace is CSL Tokyo, but remote work is also possible. People often work remotly.
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