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DIPF | Leibniz Institute for Research and Information in Education

PhD Research Assistant in Educational Technologies (AI-supported Text Coding) at DIPF | Leibniz Institute for Research and Information in Education DIPF | Leibniz Institute for Research and Information in Education in Germany

Degree Level

PhD

Field of study

Computer Science

Funding

The position is full-time and temporary from 01.04.2026 to 31.12.2029, with a salary according to EG 13 of the collective agreement for the public service of the state of Hesse (TV-H). Flexible working hours, daycare facilities, and a discounted Jobticket Germany are offered. The role supports work-life balance, diversity, and inclusion.

Deadline

Feb 9, 2026

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Country

Germany

University

DIPF | Leibniz Institute for Research and Information in Education

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Keywords

Computer Science
Education
Algorithm Design
Network Analysis
Natural Language Processing
Educational Technology
Inclusion
Content Analysis
Machine learning

About this position

The DIPF | Leibniz Institute for Research and Information in Education in Frankfurt am Main, Germany, is offering a full-time PhD Research Assistant position in Educational Technologies, focusing on AI-supported text coding. The position is part of the DFG Collaborative Research Center 1750, specifically project Z03, which addresses educational science methodologies of participation challenged by digital technologies. The project aims to develop AI-supported methodologies for educational participation and inclusion, combining qualitative research with artificial intelligence, natural language processing (NLP), and network analysis. The research will focus on processes of recognition, inclusion, justice, and participation in educational contexts.

The successful candidate will develop, train, and calibrate an AI agent for automated annotation and coding of qualitative text data, such as interviews and transcriptions. Tasks include implementing complex coding schemes in NLP pipelines, setting up annotation workflows using tools like INCEpTION or WebAnno, managing schema and guidelines, and developing AI-assisted coding functions. The role also involves evaluating labeling accuracy, analyzing trade-offs between accuracy and speed, and establishing reproducible infrastructure for data preparation, training, and experiment tracking. The candidate will be expected to complete a PhD dissertation and actively contribute to publications, workshops, and conferences.

Applicants must hold a very good master's degree in computer science, data science, mathematics, or a related field, with research experience in NLP, machine learning, or text mining. Essential skills include algorithm design, NLP pipelines, semantic text analysis, sequence labeling, and experience with modern language models. Proficiency in Python, ML/NLP tools (PyTorch, Transformers, spaCy), and MLOps/experiment tracking (DVC, MLflow, Git) is required. Very good written and spoken German and English are mandatory. The position offers a salary according to EG 13 TV-H, flexible working hours, daycare facilities, and a discounted Jobticket Germany. The DIPF promotes diversity, inclusion, and work-life balance.

To apply, submit your application as a single PDF with the usual documents, quoting reference IZB 5115-26-01, by 9 February 2026 to Prof. Dr. Hendrik Drachsler at [email protected]. For more information, contact [email protected].

Funding details

The position is full-time and temporary from 01.04.2026 to 31.12.2029, with a salary according to EG 13 of the collective agreement for the public service of the state of Hesse (TV-H). Flexible working hours, daycare facilities, and a discounted Jobticket Germany are offered. The role supports work-life balance, diversity, and inclusion.

What's required

Applicants must have a very good master's degree in computer science, data science, mathematics, or a related discipline, with relevant research experience in NLP, machine learning, or text mining. Strong skills in algorithm design, NLP pipelines, semantic text analysis, sequence labeling, and experience with fine-tuning and evaluating modern language models are required. Experience with annotation and coding workflows, proficiency in Python and ML/NLP tools (PyTorch, Transformers, spaCy), and familiarity with MLOps/experiment tracking (DVC, MLflow, Git) are essential. Very good written and spoken German and English are mandatory. Candidates should demonstrate initiative, motivation, teamwork, and the ability to work independently in an interdisciplinary environment.

How to apply

Submit your written application as a single PDF with the usual documents, quoting reference IZB 5115-26-01, by 9 February 2026. Send your application to Prof. Dr. Hendrik Drachsler at [email protected]. Contact [email protected] for more information.

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