Job Opportunities API

Constructor University in collaboration with Constructor Knowledge Labs and Constructor Technology invite applications from interested candidates for the following position PhD Position (m/f/d) in Computer Science (Wearable Intelligence & Data Fusion) 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 research. Funding The appointment provides full financial coverage through a dedicated fellowship, comprising: • Monthly stipend of €1,650 • Monthly research-cost allowance of €100 (Forschungskostenpauschale) • Health-insurance subsidy of €100 per month • Supplementary €603 mini-job allowance to support parallel part-time employment (optional) Constructor Knowledge Labs actively supports candidates in preparing applications for external funding — doctoral scholarships, foundations, or international mobility grants — and can provide institutional support and references Applicant Profile Mandatory requirements: • MSc degree (or equivalent) in Computer Science, AI/ML, Data Science, Cognitive Science, or related disciplines. • Strong background in AI/ML, signal processing, or edge computing. • Hands-on experience with   wearable or multimodal data (e.g., heart rate, EEG, activity, sleep, GPS) . • Solid mathematical and computational modeling skills. • Proficiency in academic English writing (e.g., reports, papers, theses). Preferred qualifications: • Experience with   LLMs, multimodal data fusion, or agent-based AI systems . • Familiarity with   privacy-preserving ML, dynamic consent, and GDPR-compliant frameworks . • Demonstrated ability to conduct independent research and collaborate across disciplines. • Interest in teaching, mentoring, and applied industrial research. Application Details • Deadline : August 31, 2026 Required documents: • Curriculum Vitae (CV); • Academic transcripts; • Letter of motivation outlining research interests and career goals; • 2 recommendation letters. Applications to be reviewed on a rolling basis. Shortlisted candidates  will be invited to interviews.