Multi-modal Sensing and Human-Machine Integration Adaptive AI in Tactical Environments

Position Summary

We have an open position for a postdoctoral research fellow to investigate multi‑modal sensor‑fusion approaches for understanding human behaviour in realistic environments to facilitate human‑AI adaptation at the tactical edge. The project will leverage advanced analytics on human‑derived signals from diverse systems to passively predict actions and estimate user state so that future AI platforms for dismounted Soldiers can quickly adapt to the changing needs of the Soldier and the squad.

• Lead research on sensor‑fusion techniques using physiological, biomechanical, AR/VR, and other wearable data.
• Analyze controlled laboratory studies and operational field exercises to link behavioural metrics with sensor data.
• Develop machine‑learning models, including reinforcement‑learning approaches, to predict user actions and states.
• Collaborate with a multidisciplinary team of cognitive psychologists, neuroscientists, engineers, and data scientists.
• Prepare and present research findings in internal reports and external scientific venues.

• PhD (or near completion) in engineering, computer science, data science, neuroscience, psychology, or a related field.
• Strong background in wearables, bio‑signals, and machine‑learning analytics.
• Experience with advanced machine‑learning techniques such as reinforcement learning is a plus.
• Demonstrated ability to conduct independent research and publish in peer‑reviewed venues.
• Excellent communication skills and ability to work in a multidisciplinary setting.

• Citizenship: U.S. Citizen only.
• Degree:
  Master’s or Doctoral degree (or equivalent).

Advisor:
Christopher Sinks, christopher.g.sinks.civ

Advisor:
Russell Cohen Hoffing, russell.a.cohehoffing.civ