Postdoctoral position in machine learning at Carnegie Mellon University

[ftorre]

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 j...@cs.cmu.edu and
fto...@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 1st, 2010, but later applications may be
considered until the position is filled.