All Remote

Experience with basic machine learning techniques and Python or Matlab. Experience with machine learning for audio classification (for example, Listenin, Think, and Understand, YAMnet) and/or digital signal processing for audio signals is a plus but not required; however, the candidate should have some interest in sound and auditory perception/hearing. The ideal candidate will be potentially interested in fruitful research collaborations involving measuring the soundscapes of listeners in the real world to improve technologies that treat hearing loss (e.g., hearing aids, cochlear implants).

Understanding the soundscapes listeners experience in the world is critical to designing effective interventions for listeners with hearing loss. However, characterizing soundscapes is challenging. Recent progress in machine learning has enabled improvements in classifying audio signals according to their acoustic properties. The Soundscape and Audiology Research Lab is working to leverage these improvements to understanding the interactions between the soundscape, hearing loss, hearing loss interventions, and life outcomes. In this role, the student will work closely with the PI to build, test, and apply models of sound classification for this purpose. This will involve using audio data collected in the lab to train and test models of audio classification as well as use these models to characterize the soundscapes of listeners who participate in research studies in the lab. This role can be completely remote, although some in-person meetings may be useful. Working knowledge of Python is required and some experience with Matlab is desired. Experience with basic machine learning techniques such as deep neural networks is desired. Experience with sound and digital signal processing is not required but some interest in these areas is. This is a good opportunity for a student interested in the application of machine learning to health research and/or hearing science and acoustic analysis. This work will result in research products and can include authorship as appropriate.

The ideal candidate is an upper-level undergraduate or Master's student in Computer or Data Science. The student will work closely with the PI of the lab, Dr. Erik Jorgensen, as well as collaborators in Data Science to improve existing models (e.g., Listen, Think, and Understand, YAMnet) or develop new models to classify sounds with a focus on sounds important to human life. This project will result in research products (e.g., papers, conference presentations, datasets) which can include authorship as appropriate. This role is remote with flexible hours with a pay rate of $18-20/hour. For more information about the lab, visit: https://sarl.csd.wisc.edu/

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