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

Description

In this role, you will engage in cutting-edge research on AI-driven semantic structures and adaptive content generation, while also contributing to teaching and mentoring students in various AI and machine learning disciplines.

Requirements

• •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 specified areas
• •Experience in semantic parsing and structured representation learning
• •Experience in knowledge graphs or graph-based reasoning
• •Experience in transformer models, sequence modeling, or GNNs
• •Experience in natural language generation and explainability
• •Experience in 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
• •Responsibility, self-motivation, and capability to work independently and 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

Tasks

• •Contribute to foundational research in AI-driven semantic structure extraction
• •Develop automated reasoning-flow models
• •Create adaptive content generation methods
• •Analyze and represent deep semantic and pedagogical structures
• •Extract conceptual and reasoning blocks with high fidelity
• •Generate inference-time rationale
• •Sequence content in a learner-aware manner
• •Work on semantic parsing and structured NLP
• •Develop graph-based neural models
• •Implement metacognitive prompting techniques
• •Align ontologies across disciplines
• •Optimize systems with human-in-the-loop approaches
• •Develop datasets and baseline models
• •Create personalized learning engines
• •Design reasoning-graph representations
• •Implement cross-domain mapping algorithms
• •Establish RLHF-style feedback loops for system interpretability
• •Publish high-quality research findings
• •Release research prototypes and demonstrator systems
• •Support structured semantic extraction and rationale-aware content generation
• •Assist in teaching Machine Learning and Natural Language Processing
• •Engage in course delivery and student advising
• •Supervise Bachelor and Master theses
• •Innovate methodologies for online, hybrid, and in-person learning

Benefits