AI Researcher – Multimodal Physiological Modeling

KOS AI is developing non-invasive metabolic monitoring technology that combines multimodal sensing, physiological modeling, and advanced machine learning. The AI Researcher will develop algorithms for interpreting multimodal physiological signals, modeling dynamic biological processes, building robust machine learning systems, and validating new approaches through experimental research.

Responsibilities:

• Development of new feature extraction methods for optical signals, motion signals, and physiological biomarkers
• Design of noise-resilient, motion-resilient, and artifact-robust representations
• Exploration of frequency-based, time-domain, and hybrid feature spaces
• Discovery of patterns that reflect underlying biological processes
• Research and implementation of models capable of learning from complex, noisy, and high-variability biological data
• Development of ensemble systems, temporal models, and context-aware learning frameworks
• Design of robust inference pipelines capable of running efficiently on constrained environments
• Integration of uncertainty, confidence scoring, and adaptive filtering
• Modeling of dynamic processes related to human physiology, metabolism, and behavioral context
• Exploration of relationships between physiological signals, daily patterns, and state transitions
• Development of personalized data adaptation methods that learn individual physiological baselines and trends
• Research on forecasting trends and physiological trajectories
• Identification of behavioral and biological events from multimodal data
• Development of systems that adapt to real-world, irregular, and incomplete signals
• Working with diverse datasets collected across multiple conditions
• Data cleaning, harmonization, and statistical evaluation
• Designing experiments to validate new features, models, and interpretation techniques