Postdoctoral Position in AutoML / Bayesian Optimization / Algorithm Configuration
Ludwig Maximilians University of Munich Faculty of Mathematics, Computer Science and Statistics

Postdoctoral Position in AutoML / Bayesian Optimization / Algorithm Configuration

Institution: Faculty of Mathematics, Computer Science and Statistics
(Institute for Statistics)

Date of occupation: as soon as possible

Application deadline: July 15, 2020

Grade: TV-L

Remuneration group: E14

Limitation: 2 Years

There is always the possibility of part-time employment.

The Ludwig Maximilians University Munich (LMU) is one of the most renowned and largest universities in Germany.


The chair of Statistical Learning and Data Science at the Ludwig-Maximilians-Universität in Munich, led by Prof. Dr. Bernd Bischl, in close cooperation with the Fraunhofer IIS ADA Lovelace Center, is looking for outstanding applications for a postdoc position.

The successful candidate will join Professor Bischl's research group, as well as the Fraunhofer Ada Lovelace Center in Munich and continue research in the field of AutoML, Algorithm Configuration, Meta-Learning, and Bayesian Optimization. Projects focus on research in the aforementioned fields, as well as research in application areas within the Ada Lovelace Center. The candidate will additionally aid in the supervision of Ph.D. students working in the field.

Your Responsibilities

  • Active research and publications
  • Constructing and improving software implementations
  • Support in teaching in machine learning
  • Supervising Ph.D. students

Your Profile

  • Ph.D. in statistics, machine learning, biostatistics, computer science or a related quantitative field
  • Excellent knowledge of machine learning and statistics
  • Strong programming skills (R or Python or C++) and optimally experience in working with high-performance computation clusters for benchmarking
  • Excellent academic publication track record in relevant machine learning journals and conferences
  • Strong communication and interpersonal skills, professional and confident communication with industrial partners is a strong plus
  • Strong interest in Automated Machine Learning, Algorithm Configuration, Meta Learning and Bayesian Optimization
  • Eagerness to support and supervise a team of highly motivated Ph.D. and graduate students, first experiences in team leadership is a plus
  • Fluency in written and spoken English
The university is an equal opportunity employer. Handicapped applicants will be given preference in the case of approximately equal qualifications.

LMU is interested in increasing the number of female faculty members and strongly encourages women to apply.

The position will remain open until filled and only shortlisted candidates will be notified.
The start date is as soon as possible, but negotiable.

Schwerbehinderte Personen werden bei ansonsten im Wesentlichen gleicher Eignung bevorzugt.

Additional Information

Application address

How to apply

Interested applicants should send the necessary documents in a single PDF document via email to quoting “Postdoc Application, ADA Lovelace Center” in the email subject line:

    • A short statement letter promoting you as the ideal candidate for the position (~1 page)
    • A detailed CV, with special focus on: obtained degrees, taken classes in relevant topics, publications, programming skills and projects, track record


Contact person

Dr. Juliane Lauks
Scientific Manager
Chair of Statistical Learning & Data Science
Department of Statistics
Ludwig-Maximilians-Universität München
Ludwigstr. 33
D-80539 München

Im Rahmen Ihrer Bewerbung auf eine Stelle an der Ludwig-Maximilians-Universität München (LMU) übermitteln Sie personenbezogene Daten. Beachten Sie bitte hierzu unsere Datenschutzhinweise gemäß Art. 13 Datenschutz-Grundverordnung (DSGVO) zur Erhebung und Verarbeitung von personenbezogenen Daten und die Datenschutzerklärung der LMU für den Internetauftritt. Durch die Übermittlung Ihrer Bewerbung bestätigen Sie, dass Sie die Datenschutzhinweise und die Datenschutzerklärung zur Kenntnis genommen haben und mit der Datenverarbeitung im Rahmen des Auswahlverfahrens einverstanden sind.

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