Ph.D. Researcher. Knowledge Discovery: From Unstructured Data to Shared Cognitive Maps
Salary€2k/mo
LocationConstructor Labs, Bremen, Germany
SeniorityEntry
DepartmentEducational
Company size201–500 people
PostedDec 22, 2025 · 9mo ago
Verified live10h ago
At a glanceSummarised by Seekless from the posting.
Must have6
Holding recognized MSc degree (or equivalent) in Computer Science, AI, ML, or a related discipline.
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).
Nice to have1
Students holding BSc degree and exhibiting outstanding performance and extraordinary potential can apply for fast-track PhD.
Skills
AI
ML
knowledge-graph
information retrieval
large language models
LLMs
Benefits
Health insurance
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:
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Transforming unstructured data into interactive knowledge graphs and personalized cognitive maps.
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Designing models that provide interpretable, persistent, and navigable structures of knowledge.
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Addressing challenges such as hierarchy, composability, and coarse-graining for robust, task-specific reasoning.
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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:
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Monthly stipend of €1,650
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Monthly research-cost allowance of €100 (Forschungskostenpauschale)
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Health-insurance subsidy of €100 per month
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Supplementary €550 mini-job allowance to support parallel part-time employment (optional)
Application Details
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Expected start date: September, 2026
Application package must include:
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Curriculum Vitae (CV);
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Academic transcripts ;
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A detailed letter of motivation outlining research interests and career goals;
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2 recommendation letters;
Applications to be reviewed on a rolling basis. Shortlisted candidateswill be invited to interviews.