SIGNAL PROCESSING ENGINEER
Sibel Health, based on research out of Prof. John Rogers' laboratory, is delivering better health data with soft, flexible sensors that are powered by advanced analytics, operated by best-in-class software, and integrated with the cloud optimized for artificial intelligence and machine learning.
The Signal Processing Engineer will develop algorithms and machine learning models for extracting physiological information from raw sensory information. This role will explore various literature and prototype algorithms using Python or MATLAB. This position will also be responsible for the design, development, testing, and maintenance of their works.
Please send a resume and cover letter to firstname.lastname@example.org.
- Develop algorithms to extract vital signs such as Heart Rate and SpO2 optimized for real-time use from ECG, PPG, etc.
- Create classifiers/machine learning models based on aggregated data
- Build data conversion tools (ex: the proprietary data type to ones used in industry such as EDF format)
- Test and validate works based on the open-source databases such as Physionet and UCR library
- Ad-hoc analysis and data manipulation for small scale datasets
- Identify, examine, and interpret anomalies observed in the data pipeline
- BS degree in Electrical and Computer Engineering or Computer Science
- Solid understanding of DSP fundamentals including frequency domain analysis, filtering, and the underlying mathematics
- Comprehensive understanding of machine learning techniques and algorithms, including but not limited to Neural Network, SVM, Decision Forests
- Excellent understanding of data science toolkits such as Pandas, NumPy, WeKa, etc.
- Strong analytical skills with the ability to prepare, build, investigate, and distribute big data with detail and precision
- Experience with data visualization tools such as Dash, D3.js or plotting libraries such as matplotlib, plotly, etc.
CONTACT: Czesia Eid (email@example.com)
posted May 25, 2021