PhD Candidate: AI for Semantic Reasoning & Adaptive Learning

Constructor University Bremen gGmbH; Germany (OH); On-site; USD 19,289 - 23,105; Full time

Benefits offered by this job

Monthly stipend

Health insurance subsidy

Research-cost allowance

Mini-job allowance

Job summary

Constructor University Bremen gGmbH invites applications for a PhD position focused on AI-driven semantic structure extraction, automated reasoning-flow modeling, and adaptive content generation. The role spans research on semantic parsing, graph-based reasoning, and metacognitive prompting, with cross-disciplinary collaboration across CS, linguistics, and education.

The successful candidate will contribute to datasets, baseline models, learning engines, and cross-domain mappings, while

Qualifications

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

Responsibilities

• Contribute to development of AI-driven semantic structure extraction and reasoning-flow modeling.
• Develop datasets, baseline models, and personalized learning engines.
• Publish high-quality research and release prototypes for demonstrator systems.
• Engage in teaching activities in Machine Learning, NLP, AI in Education, Knowledge Representation, and Python-based seminars.

Skills

semantic parsing

graph-based reasoning

transformer models

NLP

educational AI

Python

AI research

Education

PhD in Computer Science

Tools

Python

PyTorch

TensorFlow

Job description

Constructor University Bremen gGmbH invites applications for a PhD position focused on AI-driven semantic structure extraction, automated reasoning-flow modeling, and adaptive content generation. The role spans research on semantic parsing, graph-based reasoning, and metacognitive prompting, with cross-disciplinary collaboration across CS, linguistics, and education.

The successful candidate will contribute to datasets, baseline models, learning engines, and cross-domain mappings, while