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

Constructor University

Bremen, Germany

23 days ago

Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Working hours

Regular working hours

Job source

www.adzuna.de

Tech stack

Artificial Intelligence Cognitive Science Data Fusion Global Positioning Systems (GPS) Machine Learning Signal Processing Data Streaming Data Processing Large Language Models Information Technology Multiaccess Edge Computing

Job description

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

Requirements

PhD Position (m/f/d) in Computer Science (Wearable Intelligence & Data Fusion), * 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.