Sage Bionetworks is currently recruiting for a scientist with experience in
remote sensors, signal processing and/or machine learning. We are actively
running clinical studies that use wearable sensors and smartphones to
quantify symptoms of disease and health. You would be joining a team of
computational scientists, software engineers and clinicians to analyze data
from thousands of patients and to help design new tests to be incorporated in
future studies using smartphones.
About you: You have a background in electrical engineering, biomedical
engineering, computer science, statistics, bioinformatics or related
computational fields. You have experience working with time series data in
measuring biological processes such as accelerometers, force plates, heart
rate monitors, etc.
Specific Responsibilities Include
1. Design new modules for measuring phenotypes using sensors embedded in a
smartphone (accelerometer, gyroscope, pressure sensors, microphone, etc).
2. Extract features from sensor data and apply supervised and unsupervised
machine learning methods to quantify disease.
3. Create web-accessible analyses that are reproducible and re-usable.
Work with our software team that builds mHealth applications to drive new
4. Automate the execution of new analysis methods using scripting and
1. A graduate degree in a quantitative field such as EE, BME, applied math,
CS, bioinformatics etc.
2. Signal processing experience.
3. Experience working within a team of biologists, statisticians and software
engineers to compile, format, and analyze large, high-dimensional data sets
for downstream analysis.
1. Software development experience, including strong programming skills in a
high level language (especially Python, R or Matlab).
2. Experience with Linux shell scripting and running distributed analyses.
About Sage Bionetworks
Sage Bionetworks, is a non-profit organization located in Seattle and
dedicated to advancing biomedical research through the implementation of
reproducible, open science. Using cutting edge machine-learning
methodologies, in collaboration with scientists around the world, we build
predictive models of disease-related phenotypes through integrative analysis
of large-scale genomic and remote sensor data sets. To enhance collaborative
efforts, we provide a collaborative compute platform (www.synapse.org) for
sharing research insights in a transparent, reproducible fashion and bridge
for collecting data from mHealth applications.
To apply for this position, please send your CV and cover letter to:
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