Postdoc Position in Machine Learning for Health Sensing @ MIT

This postdoc position focuses on developing machine learning algorithms and software systems to analyze new sensor data and infer disease progression and medication efficacy. It will build on a recent MIT technology for extracting health metrics by analyzing how human bodies interact with the surrounding wireless signals. The new research will focus on how such new data type can be used in medical applications. In particular, the postdoc will lead a sub-project that deploys this sensor with patients with a particular condition (e.g., Parkinson's), and in collaboration with MIT researchers and a medical team, develop ML algorithms and systems to extract disease markers from the collected data. The research enables continuous 24/7 non-invasive health monitoring, and promises to revolutionize desease detection and treatment. The research is interdisciplinary and will span ML conferences (ICML, NIPS), wireless system conferences (MobiCom, Sensys), and medical journals.

The Wireless Center @ MIT directed by Professor Dina Katabi is seeking talented candidates for this position. The position is for a minimum of one year that can be extended to two years. The candidate should have completed (or nearly completed) a PhD in Computer Science, with a focus on machine learning or related fields such as computer vision, speech, or data mining. In particular, expertise in deep neural networks and transfer learning is highly desirable. The candidate should also have expertise in programming and software systems. No medical or wireless signal background is necessary.

To apply, send your CV and the contact information of at least two references to: Candidates will be reviewed on a rolling basis until the position is filled. Evaluation will begin on Feb 1, 2017.