Principal Investigator – Human-AI Fusion & Cognitive Wearable Intelligence

Position Overview

We are seeking a motivated early-career and visionary Principal Investigator (PI) to lead the neuroscience and cognitive modeling direction of the Human Ai fusion and cognitive.
The ideal candidate combines expertise in cognitive neuroscience, psychology, and computational modeling with strong understanding of AI-driven data analysis and practical experience with wearables.

Key Responsibilities

• Lead the research direction on Neuroscience and neuropsychology.
• Develop fusion data models linking wearable physiological and behavioral signals with cognitive and affective states.
• Design and conduct experimental studies (lab and field) to validate multimodal inference models, selecting and utilizing appropriate data sources such as EEG, heart rate, motion, and contextual signals.
• Collaborate on building the Agentic Personalization Engine (APE) that leverages cognitive modeling for adaptive, user-centered feedback.
• Guide integration of cognitive models into edge and wearable environments, defining trade-offs between accuracy, interpretability, and energy cost.
• Supervise postdocs, PhD students, and interns in interdisciplinary research combining neuroscience, machine learning, and data engineering.
• Coordinate with engineering and AI teams to ensure cognitive models are technically scalable and ethically compliant.
• Publish research results in top-tier venues and represent the project in academic and applied research communities.

Required Qualifications

• PhD in Cognitive Neuroscience, Experimental Psychology, Computational Cognitive Science, AI/ML, Data Science or a closely related discipline.
• Proven expertise in neurocognitive or behavioral data analysis, including physiological/multimodal data fusion.
• Solid understanding of machine learning, statistical modeling, and/or computational cognitive modeling.
• Track record of participating in interdisciplinary projects, preparing and submitting competitive grant applications that secured funding, and publishing in high-impact journals or conferences.
• Experience designing and executing human experiments and analyzing behavioral or physiological datasets.
• Strong ability to connect cognitive theory with computational methods.

Desirable Qualifications

• Experience with wearable sensing technologies (such as EEG headsets, smart rings, ECG, motion trackers, etc.).
• Background in Human-AI interaction or affective computing.

• Familiarity with edge AI or resource-constrained computation for real-time data processing.
• Experience building data fusion testbeds or benchmarking frameworks for cognitive or behavioral data.
• Knowledge of privacy-preserving ML, ethical constraints, dynamic consent management, and GDPR-compliant data workflows.
• Ability to collaborate across psychology, neuroscience, and AI engineering teams in both academic and applied contexts.

• Experience of leading research projects.

Project Description

This project explores how multimodal data fusion science (including wearable data streams) can be transformed into structured cognitive and behavioral insights and integrated into a knowledge-grounded digital twin (“Construct”) for bi-directional collaboration in education and research to increase the efficiency of work of scientists, students and teachers. The work centers on two complementary research directions:

1. Cognitive and Behavioral Data Fusion – Developing computational models and pipelines to integrate heterogeneous temporal data (physiological, behavioral, contextual, and cognitive), based on ground facts, collected about user into structured insight graphs. This enables a deeper understanding of user states — such as focus, engagement, and cognitive load — forming the foundation for adaptive, bi-directional context-aware personalization within the Agentic Personalization Engine (APE).
2. Human-AI Modeling & Wearable data processing– This includes exploring how compact AI models can run locally/on-edge devices to interpret user states and adapt recommendations in real time while preserving privacy and consent. 

The project will establish frameworks and benchmarks for cognitive-state modeling, privacy-preserving personalization, and ethically aligned human-AI collaboration.

Funding & Appointment Terms

Compensation will be determined based on the candidate’s profile, experience, and interview results.