Ph.D. Position in Computer Science (Wearable Intelligence & Data Fusion)

Job description

Constructor University in collaboration with Constructor Knowledge Labs and Constructor Technology About the Position The research group of Dr. Sari Sadiya at Constructor Knowledge Labs (CKL) , in collaboration with Constructor University (CU) and Constructor Technology (CT) , invites applicants for Ph.D. student positions in Computer Science with a focus on wearable intelligence, multimodal data fusion, and edge AI . The project investigates how wearable data streams can be transformed into structured knowledge and integrated into a knowledge-grounded digital avatar that supports bi-directional collaboration in education and research. Ph.D. students will work in a highly interdisciplinary environment, combining AI/ML, edge computing, cognitive science, and human–computer interaction . In close collaboration with academia and industry, they will contribute to building privacy-preserving, real-time personalization frameworks while pursuing their doctoral dissertation. About the Program The PhD program is research-centered, emphasizing original contributions in: Wearable data analytics and fusion methods Biomedical data analytics On-device processing and performance modeling Personalization and adaptive reasoning systems AI for Education Doctoral students will also have access to specialized courses in: Artificial Intelligence, Machine Learning, and Edge Computing Advanced Computational Methods and Data Science Cognitive Science and Human-Centered Computing As part of the program, students will collaborate with Constructor Technology to gain first-hand industrial experience, contributing to real-world testbeds and prototypes. Research Focus This PhD position is part of the Wearable Intelligence Project , with two main research directions: Data fusion of heterogeneous temporal streams Designing algorithms to unify multimodal signals (physiological, cognitive, contextual, scheduling, and learning data). Developing pipelines that produce structured insights powering the Agentic Personalization Engine (APE) . On-device data processing & performance modeling Developing models balancing computation, energy, and data flows across wearable, edge, and cloud environments. Exploring feasibility of running compact micro-LLMs directly on wearables . The overarching goal is to create scalable, ethical, and transparent personalization systems that support education and rese…