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Niche biomedical signal processing role with PhD preference reduces qualified applicant density.
Highly domain-specific biomedical signal and clinical validation expertise limits cross-industry transferability.
PhD/MS requirement, specialized biosignal expertise, and mandatory ML/deployment skills create very strict filters.
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Design and deploy algorithms to convert raw biometric sensor data from wearables into validated physiological metrics like Heart Rate Variability and Sleep Stages.
Develop signal processing and noise filtering methods to improve data quality from wearable sensors during movement or sleep.
Validate algorithms against clinical standards and deploy production-ready code for cloud or edge execution.
Master’s or Ph.D. in Biomedical Engineering, Computational Neuroscience, Signal Processing, Data Science, or related quantitative field.
Strong expertise in digital signal processing including time-frequency analysis and filtering techniques relevant to PPG/ECG waveform processing.
Proficiency in Python (with libraries such as NumPy, SciPy, Pandas, scikit-learn, PyTorch or TensorFlow) and SQL.
Work Experience Required: Not explicitly mentioned in the JD.
Experience working with wearable sensor data (e.g., smartwatches, chest straps, smart rings) and familiarity with clinical health data standards.
Ability to translate complex physiological signals into actionable health scores with measurable validation against clinical ground truth.
Skilled in converting prototype algorithms into production-grade modules for deployment in cloud or mobile edge environments.