Scientist Jobs

About The Position

Requirements

• Currently has or is in the process of obtaining a PhD in machine learning, computer science, hearing science, auditory or cognitive neuroscience, or a related field.
• At least 2 years of experience using Python or a similar language for data analysis and modeling.
• At least 2 years of doctoral-level experience in statistical modeling and/or machine learning, including time-series or sequential data.
• Research experience in one or more of the following: multimodal behavioral data (such as eye tracking, motion, physiological, or speech signals), speech perception, auditory attention, or conversational communication.
• Must obtain work authorization in the country of employment by the time of hire and maintain ongoing work authorization during employment.

Nice To Haves

• A record of results demonstrated through grants, fellowships, patents, or first-authored publications at venues such as ARO, ASA/JASA, Ear and Hearing, Trends in Hearing, JSLHR, ICASSP, Interspeech, ICMI, ETRA, CHI, NeurIPS, or similar.
• Experience with deep learning frameworks such as PyTorch, including multimodal or sequence models.
• Experience with wearable or egocentric sensor data, such as eye tracking, IMUs, or head-worn microphones, especially in naturalistic or conversational settings.
• Experience with hearing-impaired populations, hearing aids, or cochlear implants.
• Experience modeling with limited data, weak labels, or ambiguous ground truth.
• Experience working with large, high-dimensional datasets from multiple sources.
• Experience working and communicating cross-functionally in a team environment.
• Intent to return to a degree program after completing the internship or co-op.

Responsibilities

• Develop machine learning and statistical models to infer listening and communication behavior from multimodal wearable data, with applications to hearing assistance.
• Relate model outputs to hearing science measures, such as speech intelligibility, listening effort, and self-reported communication difficulty, to validate what the models capture.
• Design data collection strategies, benchmarks, and metrics for models trained with limited or ambiguous ground truth, including data from listeners with and without hearing loss.
• Design, run, and analyze human-subjects experiments in laboratory and naturalistic listening environments.
• Collaborate with researchers and engineers across hearing science, audio signal processing, machine learning, and perception science.
• Communicate the research agenda, progress, and full analyses of results.

Benefits

• Benefits are mentioned, but no specific benefits are listed.