Post Doc Position

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Post Doc Position

Organization: 
Tufts University
Country of Position: 
United States
Contact Name: 
Shuchin Aeron
Subject Area: 
Signal Processing Theory and Methods
Machine Learning for Signal Processing
Start Date: 
October 01, 2019
Expiration Date: 
January 01, 2020
Position Description: 

Applications are invited for a postdoctoral position in the area of machine learning and data analytics for human performance understanding and prediction, a collaborative effort at Tufts University among the Department of Electrical and Computer Engineering, Department of Computer Science, the Center for Applied Brain and Cognitive Sciences (CABCS) at Tufts University and the U.S. Army Combat Capabilities Development Command Soldier Center at Natick., MA This appointment would be for 12-18 months with an estimated start date of October 2019.

The primary project is entitled “Real time prediction of individual and team performance metric from neurophysiological measurements and team interaction data”. Under this project, the fellow will work with Tufts faculty, Drs. Shuchin Aeron, Michael Hughes, and Eric Miller, as well as CABCS scientists to develop supervised and semi-supervised machine learning algorithms that are capable of predicting cognitive state (e.g. stress) and task performance metrics (e.g. speed or marksmanship) from labeled and unlabeled multimodal physiological sensor data including information collected continuously as a function of time (e.g. accelerometer recordings or GPS trajectories) as well as data at a relatively few points in time before, during, and after a specific task (e.g. surveys and performance evaluations). 

In addition to assessing individuals, data will be collected to support the characterization of team and intergroup dynamics. We anticipate the effort will require the use of classical as well as recent developments in machine learning and in particular recurrent neural networks, deep generative models, manifold learning, and social network analysis. 

While previous experience in theoretical and applied machine learning would be ideal, we welcome applicants with significant experience in related fields including information theory, statistical signal processing, sparse signal or image processing, compressive sensing, and distributed convex optimization.   

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