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

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 ofDr. Sari SadiyaatConstructor Knowledge Labs (CKL) in collaboration withConstructor University (CU)andConstructor Technology (CT) invites applicants forPh.D. student positionsin Computer Science with a focus onwearable 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 combiningAI/ML edge computing cognitive science and humancomputer interaction. In close collaboration with academia and industry they will contribute to buildingprivacy-preserving real-time personalization frameworkswhile 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 withConstructor Technologyto gain first-hand industrial experience contributing to real-world testbeds and prototypes.

Research Focus

This PhD position is part of theWearable 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 theAgentic 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 runningcompact micro-LLMs directly on wearables.

The overarching goal is to createscalable ethical and transparent personalization systemsthat support education and research.

Funding

The appointment provides full financial coverage through a dedicated fellowship comprising:

• Monthly stipend of 1650

• 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 withwearable 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 withLLMs multimodal data fusion or agent-based AI systems.

• Familiarity withprivacy-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 candidateswill be invited to interviews.