Human Loop Optimization of Adaptive Human-Technology Systems

Overview

We are looking for a postdoctoral/research fellow to work on development and implementation of EEG‑based, passive brain‑computer interfaces (BCIs) to optimize human‑machine teams. The role involves leveraging EEG and other biosignals to passively estimate user state (cognitive load, physical load, perceived error) and facilitate interaction between humans and adaptive systems. The fellow will collaborate with neuroscientists, biomedical and mechanical engineers, and biomechanists to implement closed‑loop passive BCIs within a multidisciplinary team.

The position requires a strong computational background, expertise in online classification of human state, and integration with closed‑loop systems. The fellow will also design and carry out human‑subjects experiments to uncover novel biosignal‑based metrics that track human‑system performance in mobile scenarios.

• Develop and implement EEG‑based passive BCIs for human‑machine team optimization.
• Leverage EEG and other biosignals to estimate user cognitive, physical, and error states.
• Collaborate with multidisciplinary team to integrate BCIs into closed‑loop systems.
• Design and conduct human‑subjects experimentation to discover biosignal metrics for performance tracking.
• Support development of a test bed for research on human‑in‑the‑loop optimization of adaptive teams.

• MS/PhD or equivalent in neuroscience, biomedical engineering, computer science, or related fields.
• Expertise in machine‑learning and/or online BCI.
• Advanced programming skills in Python, Matlab, or R; strong experience in algorithmic design, mathematical models, and signal processing.
• Excellent verbal and written communication skills.
• Strong publication record.
• Ability to work independently and collaboratively with multiple investigators.

• Experience collecting and analyzing EEG.
• Advanced signal processing experience.
• Human‑subjects research experience.
• Flexibility in primary work location.