Ph.D. Researcher. Discovery: From Unstructured Data to Shared Cognitive Maps

Ph.D. Researcher. Knowledge Discovery:
From Unstructured Data to Shared Cognitive Maps Constructor University in collaboration with Constructor Knowledge Labs and Constructor Technology About the Position

The research group led by Prof. Dr. Andrey Ustyuzhanin at Constructor University, in collaboration with Constructor Knowledge Labs (CKL) and Constructor Technology (industry partner), invites applications for Ph.D. student positions in the field of Computer Science, with a focus on Artificial Intelligence (AI) and Machine Learning (ML).

This PhD position is part of an initiative to advance knowledge representation and adaptive reasoning systems. The research will focus on developing flexible frameworks for actionable knowledge representation that support storage, retrieval, and dynamic adaptation of information across diverse tasks.

• Transforming unstructured data into interactive knowledge graphs and personalized cognitive maps.
• Designing models that provide interpretable, persistent, and navigable structures of knowledge.
• Addressing challenges such as hierarchy, composability, and coarse-graining for robust, task-specific reasoning.
• Exploring individual and community-level knowledge modeling, including personalized domain maps, profile extraction from artifacts (e.g., papers, courses), and cross-domain abstraction.

The overarching goal is to create systems that enable transparent, adaptive, and spatially intuitive representations of knowledge
, supporting both individual users and collaborative communities.

• Holding recognized MSc degree (or equivalent) in Computer Science, AI, ML, or a related discipline.
• Students holding BSc degree and exhibiting outstanding performance and extraordinary potential can apply for fast-track PhD.
• Strong mathematical background supported with experience in defining and developing knowledge-graph or information retrieval systems.
• Hands‑on experience with large language models (LLMs) and their applications.
• A track record of publications in AI/ML or related areas.
• Documented experience in practical research work.
• Strong skills in academic English writing (peer-reviewed papers, reports, or equivalent).

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 €550 mini‑job allowance to support parallel part‑time employment (optional)

• Academic transcripts;
• A detailed letter of motivation outlining research interests and career goals;

Applications to be reviewed on a rolling basis. Shortlisted candidates will be invited to interviews.