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

Kurz zusammengefasst

Constructor University, a private, English‑language institution in Bremen, seeks Ph.D. researchers to join Prof. Dr. Andrey Ustyuzhanin's group. The university combines strong teaching with transdisciplinary research and collaborates closely with Constructor Knowledge Labs and Constructor Technology, an industry partner. The project focuses on artificial intelligence and machine learning, aiming to build flexible frameworks that turn unstructured data into interactive knowledge graphs and personalized cognitive maps for both individuals and communities. Day‑to‑day work will involve designing and implementing models that create interpretable, persistent, and navigable knowledge structures. Researchers will address challenges such as hierarchy, composability, and coarse‑graining to enable robust, task‑specific reasoning. The role also includes extracting profiles from artifacts like papers or courses, developing cross‑domain abstractions, and evaluating the resulting systems for transparency and adaptability. Candidates must hold a recognised MSc (or equivalent) in Computer Science, AI, ML or a related field, or an outstanding BSc for fast‑track admission. A solid mathematical foundation and proven experience in building knowledge‑graph or information‑retrieval systems are required. Hands‑on work with large language models, a record of AI/ML publications, documented practical research experience, and strong academic English writing skills are also essential. The position offers a fully funded fellowship starting February 2026, with a €1,650 monthly stipend, research‑cost allowance, health‑insurance subsidy, and an optional €550 mini‑job supplement. Applications are reviewed on a rolling basis and should include a CV, transcripts, motivation letter, and two recommendation letters. What the role asks for: - MSc degree in Computer Science, AI, ML or related - Strong mathematical background - Experience developing knowledge‑graph or information retrieval systems - Hands‑on experience with large language models - Publication record in AI/ML - Documented practical research experience - Strong academic English writing skills - Fast‑track eligibility with outstanding BSc (nice‑to‑have)