[Comp-neuro] Research Scientist: Machine Learning, Signal Processing, Neuroscience
bgholami at aretexeng.com
Thu Apr 21 22:39:40 CEST 2016
AreteX Systems, a medical technology startup company accelerating the use
of information technology in healthcare, has an immediate opening for a
Research Scientist in its office located in the New York City area.
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.
- 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
- Applicants must have received a Ph.D. degree in the seven years prior
to the application date.
- 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.
Please send your cover letter and CV to info at aretexeng.com
*About AreteX Systems:*
AreteX Systems is accelerating the use of information technology in
healthcare. Our innovative solutions help reduce intensive care unit costs
and increase quality of care. Our core technology utilizes mathematical
modeling to predict patient outcome and improve quality of care. The
technology provides an efficient method to analyze and interpret patient
data already available at the hospitals.
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