Postdoctoral Researcher in NLP & AI for Business, Economic, and Related Data in the National Research Data Infrastructure (BERD@NFDI) (m/f/x)

About us:

At the Chair of Statistical Learning and Data Science (SLDS), we develop machine learning methods that are statistically well-grounded and useful in practice, from AutoML and interpretable ML to causal, fair and probabilistic learning. We also maintain widely used open-source software such as mlr3 and OpenML.
For this position, we are joining forces with the SODA lab, which critically reflects on and improves the use of data in social science research and beyond, from its collection and analysis to evidence-based decision-making.
SODA and SLDS are both part of the Munich Center for Machine Learning (MCML), a joint LMU–TUM initiative of nearly 90 principal investigators, co-directed by Prof. Bischl, who is also a faculty member of ELLIS Munich. This places the position within a large, collaborative research community at the heart of Munich’s AI landscape.

We are looking for you:

Postdoctoral Researcher in NLP & AI for Business, Economic, and Related Data in the National Research Data Infrastructure (BERD@NFDI) (m/f/x)

in München

Your tasks and responsibilities:

BERD@NFDI is a DFG-funded consortium of the German National Research Data Infrastructure (NFDI) for Business, Economics and Related Data, with a focus on unstructured data. We are looking for a postdoc to explore how AI can support researchers across the data lifecycle, from data discovery and preparation to metadata enrichment and analysis, and what this could mean for BERD’s services and future direction.

Your Tasks and Responsibilities

• Coordinate, develop, and implement the AI strategy for BERD@NFDI, encompassing natural language processing (NLP) and machine learning (ML), and exploring potential use cases for agentic AI
• Design and evaluate ML/LLM-based workflows for BERD@NFDI services, such as the Academy, Data Portal, OCR, or the Knowledge Graph Infrastructure, with a special focus on LLM-generated data and data annotation
• Coordinate project management and administrative tasks, working closely with the task leads of the other work areas, align their activities, and shape the development of BERD@NFDI
• Contribute to consortium management, including reporting, proposals, NFDI networking, and community visibility
• Teach at the Department of Statistics
• Supervise Ph.D. and graduate students
• Conduct and publish independent high-quality research in machine learning and NLP.

Your qualifications:

• Excellent Ph.D. in statistics, computer science, machine learning, or a related field
• Strong background in machine learning and natural language processing, especially (large) language models and (text) data annotation
• Experience with advanced NLP/AI workflows, e.g., retrieval-augmented generation, synthetic data generation/annotation, or agentic systems
• Strong programming skills in Python
• Publications in relevant ML/NLP/AI venues
• Eagerness to coordinate and work in teams; first experience in project or team leadership is a plus
• Interest in research data infrastructures, FAIR principles, and open science.
• Fluency in written and spoken English; German is a plus

Benefits:

• The position offers a unique chance to explore how NLP/AI can be used in a national research data infrastructure.
• Your workplace is in the heart of Munich, just steps from the English Garden, and easy to reach by public transport.
• The position is offered for two years.
• Be part of a dynamic, international, and intellectually stimulating academic community.
• LMU Munich is an equal opportunity employer committed to excellence through diversity, and therefore explicitly encourages women to apply.

Also possible in a part-time capacity.

People with disabilities who are equally as qualified as other applicants will receive preferential treatment.

Contact:

Applications should be written entirely in English and include the following. CV with the following parts:

• Contact
• Research interests
• Education
• Research positions
• Publications
• Software & datasets
• Honours & funding
• Teaching & supervision
• Academic Service
• Technical skills
• Project management experience
• References: two referees incl. PhD supervisor, with contact details

These materials should be compiled into a single PDF and sent by November 30, 2026 at the latest to: juliane.lauks@stat.uni-muenchen.de. The e-mail subject line should include „BERD postdoc application“ and the applicant’s name.

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