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 - Department of Statistics
Remuneration group TV-L E13
Full-time / Part-time Full-time (100%)
Start date from 2024-03-01
Application deadline 2023-12-31

About us:

Join the research group on Interpretable Machine Learning & Explainable AI, part of the Chair of Statistical Learning and Data Science, led by Prof. Dr. Bernd Bischl. This position is funded by the Munich Center for Machine Learning (MCML), one of Germany's leading centers for machine learning, designed to consolidate machine learning activities at LMU Munich.

We are looking for you:

Ph.D. Position in “Interpretable Machine Learning for Tabular Data” (m/f/x)

in Munich

Your tasks and responsibilities:

The main research will focus on the advancement of interpretation methods for machine learning (ML) models trained on tabular data. Existing interpretation methods produce either local explanations for insights into individual observations or global explanations that characterize the overall behavior of a model. During your Ph.D. journey, you will conduct research on a wide range of interpretation methods, including regional explanations, which strike a balance between local and global interpretability, and other innovative methods aimed at enhancing the overall explainability of ML models.

  • Conduct research at the intersection of machine learning and statistics to improve the interpretability of predictive models trained on tabular data.
  • Publication of scientific results in internationally renowned journals and their presentation at international top-tier conferences and workshops.
  • Collaborate with fellow researchers, actively contributing to research projects and/or open-source software projects.
  • Assistance in teaching tasks and the development of course material for machine learning-related classes at the LMU such as Introduction to Machine Learning or Interpretable Machine Learning.

Your qualifications:

  • M.Sc. in statistics, mathematics, data science, computer science, or related discipline.
  • Profound knowledge of theoretical foundations in ML and statistics is mandatory.
  • Sound programming skills in R (preferred) and/or Python.
  • Knowledge or experience in interpretable machine learning (especially in post-hoc and model-agnostic methods) is an advantage, but not mandatory.
  • High sense of responsibility, reliability, personal commitment, and the ability to work independently.
  • Excellent communication and interpersonal skills and a strong team player.
  • Fluent in spoken and written English skills.


  • Fully funded 3-year position with potential for extension.
  • Interesting research projects in an exciting and evolving field.
  • A supportive scientific environment within a top-ranked German university.
  • Opportunities for international networking and exchange.
  • Comprehensive support and close collaboration with experienced researchers, including intensive supervision, guidance, and mentoring to support your success and development in and during your PhD journey

Also possible in a part-time capacity.

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


Please submit all following required documents as a single PDF file (firstname_lastname.pdf) by December 31st, 2023 via

  • Letter of motivation stating your preferred starting date (max. 1 page).
  • Detailed CV including your programming skills and a list of courses you have attended in statistics and machine learning with a brief overview of the most important topics covered within each course.
  • Certificates and transcripts of records of all university degrees obtained.
  • International applicants should include proof of English language proficiency (see recognized language certificates), not older than two years.

For further information, contact Dr. Casalicchio (Mail: and visit our Website.

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