Jobs at ML Freiburg and BCAI

Please note: the deadline for applications for the positions below has passed; we’ll have a new call for applications now. We’ll be hiring outstanding candidates at all levels, both at ML Freiburg and at the Bosch Center for Artificial Intelligence. Please stay tuned. If you saw Frank’s ICML or CVPR tutorial and would like to get in touch immediately, please email

Previous call for applications:

In the context of Frank’s ERC grant on automated deep learning (more details below), in 2019 we will have several open positions for outstanding candidates at ML Freiburg, on all of these levels:

  • Postdocs
  • PhD students
  • Research engineers
  • Interns (please note that visa issues make this tricky)

Topics of particular interest to the group include:

  • AutoML
  • Neural architecture search
  • Learning to learn
  • Meta learning and transfer learning
  • Bayesian optimization and hyperparameter optimization
  • Deep reinforcement learning
  • Optimization of neural networks
  • Evolutionary algorithms
  • Algorithm configuration and selection
  • Discrete optimization and NP-hard problem solving
  • Data-driven analysis of algorithms, hyperparameter importance, problem hardness, etc.
  • Principled empirical evaluation

Demonstrated hands-on experience in one or more of these areas is a requirement. Applicants should have an excellent first academic degree in artificial intelligence, machine learning, computer science, statistics or a related discipline.

The salary scale for full-time positions is TV-L E13 (with a monthly gross salary between 3580 EUR and 4600 EUR, depending on experience and previous position).

Application materials comprise:

  • CV
  • Full set of transcripts
  • Research statement
    • Briefly state what drives you and what are your goals in applying to ML Freiburg
  • At least 2 references
    • For each reference, please include name, title, and email address.
    • References should expect to be contacted for a reference letter.

Please submit these documents by January 10, 2019 to

Additional information

Frank’s ERC grant. This grant of 1.5 million Euros ($1.76 million US) is given for a project on automated deep learning, which facilitates the transformation of deep learning from somewhat of an art to a principled engineering science. This involves efficient neural architecture search, hyperparameter optimization, deep reinforcement learning to learn better optimizers, and a substantial component on understanding the performance of deep learning using a data-driven approach. In general, ERC grants are Europe’s most prestigious funding instrument, focused purely on an excellence-based approach to science. As stated in a recent Nature article, the ERC has helped Europe to “surpass the United States in terms of the most-cited scientific publications” and is “recognized as the best in the world in the way it supports fundamental research”.

The ML Freiburg group. Currently comprising 2 postdocs and 8 PhD students, ML Freiburg focuses on the topics of Frank’s ERC grant, and general automated algorithm design using ML in general. The group has won both international AutoML challenges held so far (2015-2016 and 2017-2018), with continuously improving versions of its widely-used open-source tool Auto-sklearn. Frank co-started the AutoML workshop series in 2014 and has been co-chairing it every year since. He also co-started the workshop series on Bayesian optimization (since 2011) and meta-learning (since 2017), and is co-editing an AutoML book. The group developed the best available out-of-the-box tool for efficient hyperparameter optimization of neural networks and is amongst the world’s leading groups in various meta-algorithmic problems, such as algorithm configuration and algorithm selection, which have led to world championship titles in SAT solving and AI planning. We collaborate closely with the other AI groups in Freiburg, especially Thomas Brox’ computer vision group, Wolfram Burgard’s robotics group, and Joschka Boedecker’s neurorobotics group. All of these groups are mostly working on deep learning and reinforcement learning these days, and we’re very excited to see great convergences and synergies resulting from this.

The University of Freiburg. Founded in 1457, the University of Freiburg is one of the oldest German universities and is now one of the nation’s leading research and teaching institutions, evidenced amongst others by it being one of the 23 members of the League of European Research Universities (LERU). It actively fosters interdisciplinary research (e.g., the Excellence cluster BrainLinks-BrainTools, which, amongst others, researches on deep learning for neuroscience), and it is one of the few universities offering world class research environments in the classical as well as in the modern disciplines. More than 24,000 students from over 100 nations are studying in 180 degree programs at 11 faculties. The university also successfully attracted the highest third-party funding per-professor in all of Germany.

Living in Freiburg. Founded in 1120, Freiburg is a lovely city with about 220.000 inhabitants in the very south west of Germany, about 30 minutes from both France and Switzerland. It is sunny, laid-back, and known for its charme and quality of life. As the gate to the Black Forest, Freiburg offers many outdoor activities, such as mountain biking, hiking, and skiing. The city was Germany’s first to have a green major and in 2010, it was voted as the Academy of Urbanism’s European City of the Year in recognition of the exemplary sustainable urbanism it has implemented over the past several decades. The #1 mode of transportation is by bike. Life is good in Freiburg 🙂