The position involves developing signal processing algorithms for innovative biomedical technologies involving physiological signals. We are looking for a self-motivated, highly talented individual with an excellent background in signal processing and machine learning. The successful candidate will work closely with a team of physicians, nurses, engineers, and scientists in designing new clinical decision support systems.

Candidates with experience in the analysis of experimental data derived from---but not limited to---auditory/visual/cross-sensory psychophysical, ECG, and galvanic-skin conductance, data would be given a higher priority.

Minimum Requirements:

• PhD in electrical engineering, computer science, biomedical engineering, neuroscience, applied mathematics, or a similar discipline.
• Knowledge of machine learning techniques.
• Experience with Python.
• Applicants must be U.S. citizens, U.S. nationals or U.S. permanent residents.
• Applicants must have received a Ph.D. degree in the seven years prior to the application date.

Preferred Qualifications:

• Prior expertise and exposure using non-invasive human physiological measures such as ECG, galvanic-skin conductance (electrodermal activity), or other categorically similar methodologies.
• Prior experience in feature extraction from physiological signals.
• Experience working with quantitative methods of neural data analysis.
• Knowledge of advanced concepts in signal processing.
• Research experience in biosignal and EEG analysis.