University of Michigan
Computer Science & Engineering
Ann Arbor, MI 48109-2121
Computational Human-Centered Analysis and Integration
Keywords: Machine learning, speech processing, and human-centered computing.
Overview: The candidate will have the opportunity to analyze spoken data from individuals with bipolar disorder, collected over the period of six months to a year. Our goal is to predict mood states from natural speech. Significant opportunities exist in machine learning, speech processing, user personalization, and health-centered computing. We are currently processing our massive ecologically valid set of speech data from individuals with bipolar disorder to uncover the acoustic characteristics of speech that will allow us to intuit mood state (manic, depressed, euthymic) and mood state change. The candidate will interact with an established team including experts in affective processing (Emily Mower Provost) and psychiatry (Melvin McInnis). We are seeking postdoctoral candidates with experience in machine learning, speech processing , and human-centered computing. Strong knowledge in programming languages and database management are required.
Human-Centered Computing problems require a human-centered way of looking at the world. If you are excited to not only develop novel algorithms and machine learning tools, but are also excited to learn more about the underlying workings of human behavior, then CHAI is looking for you! We are looking for outstanding students with a background in Digital Signal Processing and/or Machine Learning.
Please send an email to Prof. Mower Provost if you are interested in joining CHAI. Please include your CV! Please feel free to download our publications.