Human-observer based methods for measuring human motion are labor intensive qualitative, and difficult to standardize across laboratories, clinical settings, and over time. Moreover, many conditions that affect normal human movements are currently diagnosed during short visits to the clinician. Advances in wearable and wireless sensor networks have opened up new opportunities in health care systems. We are looking for a postdoc to develop novel machine learning algorithms able to perform medical diagnosis, temporal segmentation and activity recognition from accelerometer data. To qualify for the position, it is mandatory to have research experience in time series analysis. A proven record of publications in top machine learning conferences and journals is required. This will initially be a one year position with the possibility of an extension pending funding.
To apply: Applications should be sent by email to jkh(at)cs.cmu.edu and ftorre(at)cs.cmu.edu . It should include a CV, a brief statement of research interests, the expected date of availability and the names for 3 references. Applications should be sent as soon as possible and preferably before July 20th, 2010, but later applications may be considered until the position is filled.