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

Constructor Knowledge Labs; Bremen; Vor Ort; EUR 18.000 - 27.000; Vollzeit

Benefits dieser Stelle

Health-insurance subsidy

Mini-job allowance

Research-cost funding

Zusammenfassung

Constructor University, in collaboration with Constructor Knowledge Labs (CKL) and Constructor Technology, invites applications for Ph.D. student positions in Computer Science, focusing on AI and ML.

The research aims to advance knowledge representation and adaptive reasoning systems with flexible frameworks for storage, retrieval, and dynamic adaptation across diverse tasks. Funding includes a monthly stipend of €1,650, a €100 monthly research-cost allowance, a health-insurance subsidy of €100,

Qualifikationen

• MSc in CS/AI/ML or equivalent required.
• Strong mathematical background and knowledge-graph/IR experience.
• Hands-on experience with LLMs and related applications.

Aufgaben

• Transform unstructured data into interactive knowledge graphs and maps.
• Develop interpretable, persistent knowledge representations.
• Address hierarchy, composability, and robust reasoning challenges.
• Model cross-domain knowledge, including domain maps and profiles.

Kenntnisse

Knowledge graphs

Information retrieval

Large language models

Academic English writing

Publications in AI/ML

Ausbildung

MSc in Computer Science / AI / ML

BSc with outstanding performance (fast-track PhD)

Jobbeschreibung

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.

Key objectives include:

• 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.

Applicant Profile

Mandatory requirements:

• 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).

Funding & Appointment Terms

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)

Application Details

• Expected start date: September, 2026

Application package must include:

• Curriculum Vitae (CV);
• Academic transcripts;
• A detailed 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.