Employer
Constructor Labs, Bremen, Germany
Description
About the Position
The research group of Dr. Sari Sadiya at Constructor University, in collaboration with Constructor Knowledge Labs (CKL) and Constructor Technology (CT), invites applications 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:
1. 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).
2. 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.
Please mention Science HR when applying
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