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

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 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.