If you are interested in working with an awesome team on the bleeding edge of AutoML, join us! For interested BSc/MSc students, we have a separate page here. For all others, please find our current job opportunities below and join us ...

New PhD Positions

ELLIS PhD Program

We are hiring PhD candidates through the ELLIS PhD Program (call for applications here). The ELLIS PhD Program is a core pillar of the ELLIS initiative whose goal is to foster the best talent in machine learning and related fields by pairing outstanding students with leading researchers in Europe. The deadline for the student application is November 15.

DFG PhD Positions

We are happy to share the exciting news that we are leading 3 research projects in the recently accepted Cooperative Research Center "Small Data," funded by the esteemed German Research Foundation (DFG). As part of this initiative, we are pleased to announce 3 fully-paid (100% TVL13 contract) PhD positions available at our lab.

The successful candidates will have the opportunity to work on 3 cutting-edge projects titled:

  1. B01: Transfer- and meta-learning in deep networks for human brain-signal analysis
  2. C04: Learning fast and efficient hyperparameter control for deep reinforcement learning on small datasets.
  3. C05: Meta-learning for regularizing deep networks under small data regimes.

If you are passionate about this position, we encourage you to apply via the following link Please indicate the project number (e.g. B01) when applying. The application deadline is 18th June 2023.

as a Postdoctoral Researcher

Our group is looking for outstanding postdocs to lead our cutting-edge research projects, advise our bright students, and organize our world-class lectures and events. We are not only looking for the best, we also are taking the best care of our postdocs, giving them the time to further develop their skills, and many of our alumni have moved on to professorships at top universities or senior positions in big tech companies (but we still collaborate 🙂 )

To apply, you should have an excellent publication record and PhD in artificial intelligence, machine learning, computer science, statistics, or a related discipline.

Your application should contain:

  • a CV showing your academic excellence, experience, and leadership.
  • a full set of transcripts
  • a statement of purpose: Briefly state what drives you and what your goals are in applying to ML Freiburg. Note that generic applications are desk rejected. The group is very specialized, demonstrate that you are aware of our work and relate this to your own interests and expertise. We also encourage you to highlight one of your own publications that you think would be of particular interest to us.
  • At least two reference letters/writers:
    • For each reference, please include their name, title, and email address.
    • References should expect to be contacted for a reference letter.

Please submit all these documents to For security reasons, do not send us archive (e.g., .zip) files (we will not open them). Failing to do so, your application will be desk rejected.

FYI: The salary scale for full-time positions is TV-L E13 to TV-L E14, 100%. This is very high in comparison to Postdocs elsewhere in Europe, and even some universities in Germany strangely only offer 75% positions.

as a PhD Student

Please apply through the central European Laboratory for Learning and Intelligent Systems (ELLIS) portal: and also notify us of your application by email to
The deadline for applications through the ELLIS program is on November 15th. If you for some reason cannot apply through ELLIS, feel free to also apply directly following the 'join us as a postdoctoral researcher' instructions. Make sure to motivate why you care about a position in our lab even if you apply through ELLIS! Failing to do so, your application will be desk rejected.

FYI: The salary scale for full-time positions is TV-L E13, 100% (a very good salary for living in Freiburg).

as a Research Engineer

Our group is looking for outstanding Research Engineers to help bring our world-class research to real-world users and practitioners. We love open-source and believe in open, reproducible research.

We need people with practical skills in data-science, machine learning, scripting and large-scale experiments to make this happen. Our github repos have several thousand stars and are used and downloaded by people daily. While we hold our code to a high standard, more users brings more use cases and we have a need to maintain and help advance our research as usable, robust tools.

Another aspect of a research engineer we would love to have is someone who is interested in making our awesome researchers produce even more awesome research. Internal tooling and intimate knowledge of engineering aspects of the tools we use can help everyone produce an even greater impact. 

An ideal research engineer has used our tools before, but, having experience in some of our workflows will definitely prove useful:

  • Our group primarily uses Linux with some using Mac/Windows, we are command line users, using bash and Python scripts to get tasks done.
  • A broad knowledge of data-science and machine learning is essential but deep knowledge of intricacies is not. You should have some familiarity with some of our topics such as NAS, HPO, RL, AC, meta-learning, xAutoml, … reading and understanding some of our publications is a must. 
  • We use github to open-source all of our software. We work with git and have our github workflows, contributing to open-source is something we do almost every day. 
  • Virtually all of our code is Python and it’s eco-system of libraries, pandas, numpy, scipy, Pytorch, scikit-learn, gym, …
  • Part of research is conducting large scale experiments and so we use slurm and Moab to distribute across our high computing clusters.
  • Reproducibility, documented and long lasting code is a core principle we try to have with some of our more popular libraries auto-sklearn, auto-pytorch, SMAC, …

To apply:

  • We require a CV highlighting your experience
  • A statement about why you would like to work with us
  • Code you are proud to show and talk about
  • Anything else you would like to present to us related to your candidacy

Please submit these documents to For security reasons, do not send us archive (e.g., .zip) files (we will not open them). Failing to do so, your application will be desk rejected.

FYI: The salary scale for full-time positions is TV-L E13.

as a Visiting Researcher / for an Internship

If you are currently doing a postdoc or PhD in a field very closely related to our group’s research and would like to visit our group, please email Similarly strict selection criteria apply as for postdocs and PhD students we hire — we only accept outstanding candidates (please add your CV, transcripts, and statement of purpose to your application).

Reimbursement: Our possibilities to reimburse visiting researchers are limited. If you are self-funded, please clearly mention this in your application.

Internships: If you do not have a MSc degree, there is no possibility for doing an internship with us. Rather, we encourage you to apply to our MSc programme. Information on this programme and how to apply can be found here: We note that in the AI masters program you can basically fill your entire curriculum with AI courses.

DAAD Wise program: We do NOT accept applications for DAAD internships anymore.

Need Additional Reasons to Join us?

Frank’s ERC Consolidator grant (and his previous 2 ERC grants). This grant of 2 million Euros (roughly $2 million US) allows us to do basic research on the foundations of the next generation of deep learning, no strings attached (other than doing excellent science). Please see this blog post on Deep Learning 2.0 for details about the project. 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. ML Freiburg is amongst the leading groups in AutoML worldwide, with a focus on automated deep learning. This includes meta-learning (e.g., with 10 papers at the NeurIPS meta-learning workshop), efficient neural architecture search, efficient hyperparameter optimization, deep learning for tabular data, and automated algorithm design using ML in general. The group has won the first two international AutoML challenges (2015-2016 and 2017-2018), with continuously improving versions of its widely-used open-source tool Auto-sklearn, is also building the automated deep learning tool Auto-PyTorch and proposed the radically different approach of prior-data-fitted networks, which learned to do tabular classification in a single forward pass while approximating Bayesian inference. Frank is the general chair of the AutoML conference, after co-organizing the AutoML workshop series at ICML for eight years in a row. He also co-started the workshop series on Bayesian optimization at NeurIPS (since 2011), meta-learning at NeurIPS (since 2017), neural architecture search at ICLR (since 2020) and co-edited the 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 neural architecture search and various other meta-algorithmic problems, such as algorithm configuration and algorithm selection, which have led to world championship titles in SAT solving and AI planning.

Resources and collaboration opportunities. ML Freiburg owns several large compute clusters, comprising about 300 GPUs and 1500 CPU cores. It also has (non-exclusive) access to a central cluster with 15.000 CPU cores. The group collaborates closely with the Bosch Center for Artificial Intelligence, which funds basic research on AutoML in the group with 6.4 million euros over 4 years. We also collaborate closely with the other AI groups in Freiburg, especially Thomas Brox’ computer vision group (leading to several new advances in neural architecture search), Tonio Ball’s neuromedical AI lab (leading to the first published work achieving state-of-the-art performance in EEG decoding with deep learning), the robotics group, Joschka Boedecker’s neurorobotics group, Abhinav Valada’s robot learning group and Josif Grabocka’s representation learning group. All of these groups are mostly working on deep learning and reinforcement learning these days, leading to exciting convergences and synergies. Frank is also Chief Expert AutoML at the Bosch Center for Artificial Intelligence. Finally, Frank is the director of the ELLIS unit Freiburg (one of the founding 17 units), and Frank is a fellow in the ELLIS programme on robust machine learning, opening up many opportunities for collaboration with other machine learning hot spots in Europe, such as the university of Oxford. The group also has close ties to many researchers in top industrial labs, including DeepMind, Google, Microsoft, Meta, Bosch and Samsung.

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.

The University of Freiburg is one of the 17 first excellent European universities that have been distinguished as ELLIS units for their excellence in machine learning. The university has a particularly strong group of faculty members in the field of artificial intelligence (including 3 ERC grant holders) covering a wide range of modern AI topics, such as machine learning, computer vision, robotics, planning, and control. The yearly budget in AI is more than 3M Euro and funds more than 50 PhD students. A robotics school and a deep learning compute cluster have been fostering the interdisciplinary research among AI researchers via the overarching methodology of deep learning. There are strong international relationships to other scientists and companies with an adjunct professor from DeepMind and three faculty members working
part time for Bosch, Toyota, and Amazon, respectively, and a faculty member advising the German parliament on AI. The AI research at the University transfers its results actively to other disciplines, such as the life sciences, sustainable systems engineering, and ethics. The AI researchers are involved in all four clusters of excellence as set up by the German government, one of these clusters being coordinated by a faculty member in AI.