PhD Researcher (m/f/d) AI for Semantic Structures, Reasoning Flows & Personalized Content Generation

Constructor University Bremen gGmbH; Bremen; On-site; EUR 24,000 - 29,000; Full time

Benefits offered by this job

Fellowship support

Health-insurance subsidy

Mini-job allowance (optional)

Job summary

Constructor University Bremen gGmbH invites applications for a PhD Researcher (m/f/d) in AI for Semantic Structures, Reasoning Flows & Personalized Content Generation. You will contribute to AI-driven semantic extraction, automated reasoning flows, and adaptive educational content, collaborating across CS, computational linguistics, and learning sciences.

The role includes developing datasets, publishing findings, teaching activities at BSc/MSc/PhD levels, and joining a supportive fellowship

Qualifications

• Master’s or PhD in Computer Science, Artificial Intelligence, Machine Learning, Computational Linguistics, or related field.
• Strong research potential evidenced by publications, thesis, or projects.
• Interest or experience in innovative teaching and learning methods.

Responsibilities

• Develop datasets, baseline models, and reasoning-graph representations.
• Publish research results and contribute to demonstrator systems.
• Support teaching activities across undergraduate to PhD levels.
• Collaborate across computer science, linguistics, cognitive science, and learning sciences.

Skills

Semantic parsing

Graph-based reasoning

Transformer models

Natural language generation

Educational AI

Cognitive modelling

Interdisciplinary research

Teaching innovation

Education

Master’s or PhD in CS/AI/ML

Tools

Python

Job description

Become a part of our journey and join the Constructor Community as:

PhD Researcher (m/f/d): AI for Semantic Structures, Reasoning Flows & Personalized Content Generation

You are expected to contribute to the development of internationally visible, foundational research in AI-driven semantic structure extraction, automated reasoning-flow modeling, and adaptive content generation. The research focuses on methods for analyzing and representing deep semantic and pedagogical structures in scientific and educational materials; high-fidelity extraction of conceptual and reasoning blocks; inference-time rationale generation; and adaptive, learner-aware sequencing of content. This includes work on semantic parsing, structured NLP, graph-based neural models, metacognitive prompting, ontology alignment across disciplines, and human-in-the-loop optimization.

In this context, interdisciplinary research is strongly encouraged—particularly collaborations spanning computer science, computational linguistics, cognitive science, and the learning sciences. You will contribute to developing datasets, baseline models, personalized learning engines, reasoning-graph representations, cross-domain mapping algorithms, and RLHF-style feedback loops that improve system interpretability and instructional quality. The successful candidate will also contribute to high-quality publications, release research prototypes, and support demonstrator systems that deliver structured semantic extraction, rationale-aware content generation, and cross-domain transfer of reasoning structures.

Additionally, the successful candidate is expected to support teaching activities in areas such as Machine Learning, Natural Language Processing, AI in Education, Knowledge Representation, and Python-based analytical seminars at the BSc, MSc, and PhD levels. Responsibilities include assisting in course delivery, advising students, supervising Bachelor/Master theses, and engaging in methodological innovation for online, hybrid, and in-person learning environments.

The university provides strong support for early-career researchers, including mentorship, administrative assistance, access to computational resources, conference funding, and opportunities to collaborate with other research groups and industrial partners working at the intersection of AI and digital education.

Your Profile

Master’s or PhD degree in Computer Science, Artificial Intelligence, Machine Learning, Computational Linguistics, or a related field.

Strong research interest and practical experience in one or more of the following areas:

• semantic parsing and structured representation learning
• knowledge graphs or graph-based reasoning
• transformer models, sequence modeling, or GNNs
• natural language generation and explainability
• educational AI, personalization algorithms, or cognitive modeling

Evidence of research potential through publications, a strong thesis, or significant projects.

Experience or interest in innovative teaching and learning approaches.

Ability to translate theoretical insights into engineered prototypes and systems supporting scientific or educational use cases.

Responsible, self-motivated, and capable of working independently as well as collaboratively.

Excellent verbal and written communication skills.

Intercultural competence and experience in international environments.

Fluency with co-pilot tools for coding and writing.

Fluency in English, the language of instruction and communication on campus.

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)