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Postdoctoral Researcher: AI for Semantic Structures, Reasoning Flows & Personalized Content Generation

Constructor Knowledge Labs
Bremen

Jetzt bewerben

Details zur Stelle

Vollzeit

Qualifikationen

TensorFlow
Doktorat
Informatik
Schreibfähigkeiten
Englisch
Forschungserfahrung
Master
Bachelor
Machine Learning
Computerlinguistik
Künstliche Intelligenz
Python

Vollständige Stellenbeschreibung

About the position

You are expected to conduct and lead internationally competitive research in AI-driven semantic analysis and automated reasoning-flow modeling, with applications to educational materials, scientific documents, and research authoring. Your work will focus on developing advanced methods for semantic structure extraction, conceptual and argumentative flow reconstruction, rationale-aware content generation, metacognitive prompting, and adaptive personalization. Core research contributions will include designing algorithms for concept and structure extraction, building neural/graph hybrid models for pedagogical reasoning, implementing ontology-alignment methods for cross-domain transfer, and creating human-in-the-loop optimization pipelines that incorporate expert feedback—aligning with the multi-year research roadmap (WP1–WP3) .

As a postdoctoral researcher, you will take a leading role in designing experiments, shaping research directions, supervising junior researchers (including PhD students), and contributing to the development of end-to-end demonstrator systems capable of generating structured, rationale-rich, and learner-adaptive content. You will be responsible for coordinating data annotation efforts, overseeing the construction of evaluation frameworks, and driving publication-quality research outputs, including survey papers, methodology papers, and cross-domain transfer studies. Interdisciplinary collaboration is strongly encouraged, especially at the intersection of computer science, computational linguistics, cognitive science, and learning technologies, and you will have opportunities to initiate or co-lead joint projects with internal and external partners.

The postdoctoral researcher will also contribute to teaching in areas such as Machine Learning, NLP, AI for Education, Explainable AI, and Python-based applied seminars, supporting course development and supervising Bachelor's, Master's, and PhD theses. The university provides a supportive academic environment—including mentoring, administrative support, computational resources, and opportunities for technology transfer and industry engagement—enabling the postdoctoral researcher to establish or strengthen an internationally visible research profile.

Mandatory requirements

PhD degree in Computer Science, Artificial Intelligence, Machine Learning, Computational Linguistics, or a closely related field.
Strong publication record in relevant top-tier venues (e.g., NeurIPS, ACL, ICML, ICLR, AIED, LAK, AAAI).
Demonstrated expertise in at least two of the following areas:
- semantic parsing or structured NLP
- knowledge representation, reasoning graphs, or GNNs
- natural language generation, explainability, or rationale modeling
- ontology alignment, cross-domain transfer, or representation learning
- personalization algorithms, metacognition, or educational AI
- human-in-the-loop systems or RLHF
Experience in leading research tasks, mentoring junior researchers, and coordinating multi-stage projects.
Fluency in Python and modern deep learning frameworks (PyTorch/TensorFlow).
Strong analytical, communication, and academic writing skills.
Demonstrable ability to design rigorous experimental pipelines and validation methodologies.
Responsible, independent, and proactive research personality with strong teamwork skills.
Intercultural experience and readiness to work in an international research environment.
Fluency with AI co-pilot assistants for coding and writing.
Fluency in English, the primary language of research and instruction on campus.

Preferred qualifications:

Experience in developing research prototypes, evaluation benchmarks, or large-scale datasets.
Prior involvement in interdisciplinary or applied research projects.
Potential to contribute to competitive funding applications.
Experience with open-source releases or reproducible research pipelines.

Application Details

Starting date: March, 2026
Contract: Full-time (100%), TV-L E13
Work mode: Hybrid

Application package must include:

Curriculum Vitae (CV);
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.

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