Ludwig-Maximilians-Universität München is a leading research university in Europe. Since its founding in 1472 it has been committed to the highest international standards of excellence in research and teaching.

Institution Faculty of Mathematics, Informatics and Statistics Remuneration group BesGr A14 Start date As soon as possible

Professorship of Statistics and Data Science in Official Statistics (Thuringian Model)‚ combined with the Head of an organizational unit (from section head level upwards) at the Federal Statistical Office

The Faculty of Mathematics, Informatics and Statistics, Ludwig-Maximilians-Universität München (LMU) and the Federal Statistical Office (Statistisches Bundesamt, Destatis) invite applications for a Professorship of Statistics and Data Science in Official Statistics according to Art. 67 (3) of the Bavarian Higher Education Innovation Act (Thuringian Model)‚ combined with the Head of an organizational unit (from section head level upwards) at the Federal Statistical Office commencing as soon as possible.

Location: Wiesbaden/Munich

The professorship is based at the Institute of Statistics within the Faculty of Mathematics, Informatics and Statistics at LMU Munich and is combined with a permanent leadership position (from section head level upwards) at the Federal Statistical Office.

Employment at the Federal Statistical Office includes holding a leadership role (from section head level upwards; at least A14 German civil service pay grade or pay group 14 TVöD; for more information about your expected salary, please use the ). Responsibilities include both disciplinary and technical leadership, as well as the implementation of new methods in official statistics.

The professorship at LMU Munich ensues in accordance with Art. 67 (3) BayHIG, i.e. without establishing a civil servant or employment relationship with LMU Munich or the Free State of Bavaria. For the duration of employment at the Federal Statistical Office, the appointed individual is granted membership status at LMU Munich within the group of full-time university professors.

The responsibilities of the professorship include teaching and research in the field of official statistics. The position is expected to combine the research focus of the LMU Department of Statistics in statistics, data science, and artificial intelligence with the mission of the Federal Statistical Office to further develop statistical methods and technologies for federal purposes.

The research program is intended to support the long-term development and modernization of official statistics production. The goal is to explore new methods of data processing, data usage, and analytical techniques, evaluate their potential in statistical production, and integrate them into official statistical systems.

In addition, the professorship is expected to strengthen and expand teaching in applied methodology in official statistics at LMU Munich. The teaching load is two semester hours per week (2 SWS). Participation is expected in the EMOS program, the Munich School for Data Science (MUDS), and the Munich Center for Machine Learning (MCML). Cooperation with the institute’s consulting centers (StabLab, Machine Learning Consulting Unit) is also desirable.

LMU Munich and the Federal Statistical Office are seeking highly qualified researchers who, following completion of a university degree and an above-average doctorate (or equivalent outstanding qualification), have demonstrated exceptional potential for an academic career, as well as the ability to conduct independent research and teaching at a level appropriate for a professorship. Experience in managing staff is also required.

LMU Munich and the Federal Statistical Office aim to increase the proportion of women in leadership roles as well as in research and teaching, and therefore strongly encourage female academics to apply.

We value diversity and therefore welcome all applications—regardless of gender, nationality, ethnic and social background, religion/beliefs, disability, age, or sexual orientation and identity. We are pleased to receive applications from candidates of all nationalities and particularly welcome applications from people with a migration background. Applicants with disabilities will be given preference if they are equally qualified and capable. Only a minimum level of physical fitness is required. The position is generally suitable for part-time work. It will be possible to consider part-time models in individual cases.

LMU Munich is an equal opportunity employer. The University continues to be very successful in increasing the number of female faculty members and strongly encourages applications from female candidates. LMU Munich intends to enhance the diversity of its faculty members.

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

Contact:

Please submit your application comprising a curriculum vitae, documentation of academic degrees and certificates as well as a list of publications only in electronic form as a single pdf-file, not larger than 10MB to the Dean of the Faculty of Mathematics, Informatics and Statistics, Ludwig-Maximilians-Universität München, using the following link no later than 30 September 2026 and in parallel in electronic form exclusively to the Federal Statistical Office ().

Applications sent by email are transmitted unencrypted. For the option of encrypted transmission, please contact in advance. By submitting your application documents, you consent to the processing of the data contained therein for the purpose of the appointment procedure for this professorship. Further information on the collection and processing of personal data for applicants can be found online at: and .

Information regarding the academic focus of the position and the advertised position at the Federal Statistical Office can be obtained from Göran Kauermann, LMU () and Markus Zwick, Federal Statistical Office ().

In the course of your application for an open position at Ludwig-Maximilians-Universität (LMU) München, you will be required to submit personal information. Please be sure to refer to our . By submitting your application, you confirm that you have read and understood our data protection guidelines and privacy policy and that you agree to your data being processed in accordance with the selection process.

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