Academic Staff Member (f/m/d) ID no. 308/2026

University of Potsdam, Faculty of Science

Organisation/Company University of Potsdam, Faculty of Science Department Institute of Computer Science/Chair of AI in the Sciences Research Field Computer science » Other Researcher Profile First Stage Researcher (R1) Recognised Researcher (R2) Positions Other Positions Application Deadline 22 Mar 2026 - 23:59 (Europe/Berlin) Country Germany Type of Contract Temporary Job Status Full-time Hours Per Week 40 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No

Offer Description

TheFaculty of Science/Institute of Computer Science/Chair of AI in the Sciences at the University of Potsdam invites applications for the following position limited until June 30, 2029, to be filled as soon as possible subject to funding approval:

Academic Staff Member (f/m/d)

ID no. 308/2026

The position is full-time (40 hours per week, 100 %). The fixed term of employment is in accordance with Section 2 subsection 1 of the German Act on Fixed-Term Employment Contracts in Science and Academia (Wissenschaftszeitvertragsgesetz or WissZeitVG). An extension may be possible if personal and legal requirements are met.

Your Field of Work:

We are seeking an outstanding and highly motivated researcher to support the research in the intersection of explainable artificial intelligence (XAI) and causal inference. Our goal is to develop AI systems that are causally understandable, such that predictions can be interpreted in terms of underlying mechanisms, structural dependencies, and intervention effects, which is especially important for real-world applications.

The postdoc position is connected to the Collaborative Research Centre (CRC)1294 “Data Assimilation” and the University of Potsdam’s chair of AI in the Sciences.

Within this framework, we seek to:

• Move beyond post-hoc explanations and integrate causal inference with mechanistic model understanding
• Link concept-based explanations in deep neural networks to structural causal models
• Enable AI systems to support interventional reasoning

Our ambition is to bridge mechanistic understanding and machine learning, especially in complex, dynamic real-world systems.

The Scope of Your Responsibilities:

• Development and extension of methods in causal inference and explainable AI, including causal effect estimation, causal discovery, and concept-based explanation techniques
• Integration of causal and XAI methods into machine learning pipelines within the CRC 1294 “Data Assimilation”
• Implementation, evaluation, and benchmarking of novel algorithms on synthetic and real-world datasets
• Development and maintenance of open-source research software and reproducible workflows
• Collaboration with CRC partners across disciplines (e.g., hydrology, climate science, bioeconomy) to apply and validate methods
• Dissemination of results through publications in high-impact journals and conferences
• Presenting research results on conferences, seminars or selected teaching sessions
• Co-supervision of students

Further academic qualification (doctorate or post-doctoral Habilitation thesis) is possible. At least one-third of working hours is available for in-depth academic work.

Your Qualifications:

• Master’s or PhD degree with strong research experience in computer science, mathematics, statistics, physics, or a related quantitative discipline
• Proven experience in causal inference or explainable AI, ideally documented through peer-reviewed publications
• Strong programming skills (preferably in Python), with experience in developing research software
• Solid foundation in machine learning and statistical modeling
• Excellent communication skills and ability to work in interdisciplinary teams

We are also looking for the following competencies:

• Experience with probabilistic graphical models, time series analysis, or deep learning
• Familiarity with reproducible research practices and open-source collaboration
• Interest in interdisciplinary applications (e.g., in the natural or environmental sciences)
• Ability to manage codebases and computational experiments efficiently
• Motivation to contribute to academic publications and joint research initiatives

What We Offer:

As a university, we combine the strong potential for development of a teaching and research institution with the attractive working conditions of the public sector. The University of Potsdam is a reliable employer that supports its employees with a variety of offers and benefits:

• Make the most of the various continuing education and networking opportunities offered by the University of Potsdam to refine your subject-specific and interdisciplinary competencies for professional as well as personal growth.
• All campuses have good transport connections. You can receive a monthly subsidy for the public transport job ticket and use our campus bicycles.
• Benefit from a company pension scheme, a special annual payment, and capital-forming benefits.
• Take advantage of the various offers from our Occupational Health Management unit as well as the Academic Sports Center.
• To improve employees’ work-life balance, the University of Potsdam offers family-friendly flexible working hours and a defined share of remote working hours (e.g. work from home).
• You have 30 vacation days per year (with a 5-day week) and are also exempt from work on December 24 and 31.

For further information about this position, please contact Mr. Jakob Runge by e-mail: or telephone: 0331 / 977 -

Your Application:

Please send us your application including the ID no. 308/2026 by March 22, 2026 by email toSecretariat of the Chair

The University of Potsdam values the diversity of its community and pursues the goals of equal opportunity regardless of gender, nationality, ethnic and social origin, religion/belief, disability, age, and sexual orientation and identity. Applications from abroad and from persons with a migration background are expressly encouraged. The university strives for a balanced gender ratio in all employment groups; in areas where women are underrepresented, women are given preference in case of equal suitability (Section 7 paragraph 4 of the Brandenburg Higher Education Act). People with disabilities are given preferential consideration in case of equal suitability. In aptitude tests and selection interviews, individual compensation measures for disadvantages are granted, taking the specific disability into consideration. If a person with a disability would like to make use of individual compensation measures, please state this in the application letter.

Where to apply

E-mail

Requirements

Research Field Computer science » Other Education Level Master Degree or equivalent

Research Field Computer science » Other Education Level PhD or equivalent

Research Field Mathematics » Statistics Education Level Master Degree or equivalent

Research Field Mathematics » Statistics Education Level PhD or equivalent

Skills/Qualifications

• Master’s or PhD degree with strong research experience in computer science, mathematics, statistics, physics, or a related quantitative discipline
• Proven experience in causal inference or explainable AI, ideally documented through peer-reviewed publications
• Strong programming skills (preferably in Python), with experience in developing research software
• Solid foundation in machine learning and statistical modeling
• Excellent communication skills and ability to work in interdisciplinary teams

Specific Requirements

• Experience with probabilistic graphical models, time series analysis, or deep learning
• Familiarity with reproducible research practices and open-source collaboration
• Interest in interdisciplinary applications (e.g., in the natural or environmental sciences)
• Ability to manage codebases and computational experiments efficiently
• Motivation to contribute to academic publications and joint research initiatives

Number of offers available 1 Company/Institute University of Potsdam, Faculty of Science/Institute of Computer Science/Chair of AI in the Sciences Country Germany City Potsdam Postal Code 14476 Geofield

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