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

About the role

<p>Constructor University in collaboration with Constructor Knowledge Labs and Constructor Technology</p> <p><strong>About the Position</strong></p> <p>The research group of <a href="https://sari-saba-sadiya.github.io/">Dr. Sari Sadiya</a> at <strong>Constructor Knowledge Labs (CKL)</strong>, in collaboration with <strong>Constructor University (CU)</strong> and <strong>Constructor Technology (CT)</strong>, invites applicants for <strong>Ph.D. student positions</strong> in Computer Science with a focus on <strong>wearable intelligence, multimodal data fusion, and edge AI</strong>.</p> <p>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.</p> <p>Ph.D. students will work in a highly interdisciplinary environment, combining <strong>AI/ML, edge computing, cognitive science, and human–computer interaction</strong>. In close collaboration with academia and industry, they will contribute to building <strong>privacy-preserving, real-time personalization frameworks</strong> while pursuing their doctoral dissertation.</p> <p><strong>About the Program</strong></p> <p>The PhD program is research-centered, emphasizing original contributions in:</p> <ul> <li><strong>Wearable data analytics and fusion methods</strong></li> <li><strong>Biomedical data analytics</strong></li> <li><strong>On-device processing and performance modeling</strong></li> <li><strong>Personalization and adaptive reasoning systems</strong></li> <li><strong>AI for Education</strong></li> </ul> <p>Doctoral students will also have access to specialized courses in:</p> <ul> <li>Artificial Intelligence, Machine Learning, and Edge Computing</li> <li>Advanced Computational Methods and Data Science</li> <li>Cognitive Science and Human-Centered Computing</li> </ul> <p>As part of the program, students will collaborate with <strong>Constructor Technology</strong> to gain first-hand industrial experience, contributing to real-world testbeds and prototypes.</p> <p><strong>Research Focus</strong></p> <p>This PhD position is part of the <strong>Wearable Intelligence Project</strong>, with two main research directions:</p> <ul> <li><strong>Data fusion of heterogeneous temporal streams</strong></li> <ul> <li>Designing algorithms to unify multimodal signals (physiological, cognitive, contextual, scheduling, and learning data).</li> <li>Developing pipelines that produce structured insights powering the <strong>Agentic Personalization Engine (APE)</strong>.</li> </ul> <li><strong>On-device data processing &amp; performance modeling</strong></li> <ul> <li>Developing models balancing computation, energy, and data flows across wearable, edge, and cloud environments.</li> <li>Exploring feasibility of running <strong>compact micro-LLMs directly on wearables</strong>.</li> </ul> </ul> <p>The overarching goal is to create <strong>scalable, ethical, and transparent personalization systems</strong> that support education and research.</p> <p><strong>Funding </strong></p> <p>The appointment provides full financial coverage through a dedicated fellowship, comprising:</p> <ul> <li>Monthly stipend of €1,650</li> <li>Monthly research-cost allowance of €100 (Forschungskostenpauschale)</li> <li>Health-insurance subsidy of €100 per month</li> <li>Supplementary €603 mini-job allowance to support parallel part-time employment (optional)</li> </ul> <p>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</p> <p><strong>Applicant Profile</strong></p> <p><strong>Mandatory requirements:</strong></p> <ul> <li>MSc degree (or equivalent) in Computer Science, AI/ML, Data Science, Cognitive Science, or related disciplines.</li> <li>Strong background in AI/ML, signal processing,

Skills