Zuse School ELIZA

The Konrad Zuse School of Excellence in Learning and Intelligent Systems (ELIZA) is a graduate school in the field of artificial intelligence (AI) funded by the German Academic Exchange Service (DAAD). ELIZA’s research and training activities focus on four main areas: the basics of machine learning (ML) — including ML-driven fields like computer vision, Natural Language Processing (NLP), or robot learning —, machine learning systems, applications in autonomous systems, as well as trans-disciplinary applications for machine learning in other scientific fields, from life sciences to physics.

Training AI talents in a strong alliance

The graduate school offers students a combination of excellent, research-based education at the Master’s and doctoral level, supervision provided by internationally renowned mentors from both academia and industry, and networking opportunities across different sites. Coordinated by TU Darmstadt, ELIZA brings together research institutes from seven German cities. They work together under the umbrella of the European Laboratory for Learning and Intelligent Systems (ELLIS), Europe’s leading academic network for machine learning-focused AI.

Please note: Our permanent web presence will be available later in 2024 under www.eliza.school.

Professor Stefan Roth,
Director, Zuse School ELIZA

In ELIZA, we bring together Germany’s leading experts in machine learning-driven AI. Our Zuse School connects seven German units of the European ELLIS network into a truly distributed graduate school of excellence in modern AI with an extensive network of renowned industry partners.

The ELIZA School will make scientific excellence in learning and intelligent systems the cornerstone of its research and educational activities, with focus areas on

  1. foundations of ML (which includes ML-driven disciplines such as computer vision, natural language processing, or robot learning),
  2. ML systems,
  3. applications in autonomous systems, and
  4. across all areas of science, ranging from the life sciences to physics.

ELIZA builds upon ELLIS, the European Laboratory for Learning and Intelligent Systems, Europe’s leading academic network for machine learning-focused AI, recently recognized for its innovation by the German AI Award 2021 (Deutscher KI-Preis 2021). In ELIZA we connect seven competitively selected German ELLIS units (Berlin, Darmstadt, Freiburg, Heidelberg, München, Saarbrücken, Tübingen) together with their academic institutions; select faculty members from each unit form the set of currently 48 academic fellows of ELIZA, 35 of which are also fellows of ELLIS, its highest internationally peer-reviewed member grade. Industrial fellows, appointed by an Industrial Relations Board, will complement the School and provide direct links into the AI economy in Germany and beyond.

 

ELIZA Sites and Participating Institutions

ELIZA connects seven competitively selected German ELLIS units.
ELIZA connects seven competitively selected German ELLIS units.

Berlin:

Darmstadt:

Freiburg:

Heidelberg:

München:

Saarbrücken:

Tübingen:

 

Networking ELIZA with ELLIS

ELLIS is a highly visible European grassroots initiative for modern AI that we leverage, by adding sustained network funding and national backing to realize its benefits and to support outstanding Master’s and PhD students at the participating units. ELIZA’s vision is to build an outstanding foundation within ELLIS for research excellence in machine learning-driven AI. Specifically, we want to

  1. connect international AI talent and German academia,
  2. support students from groups that are traditionally underrepresented in AI,
  3. create a highly attractive and tightly interwoven research and educational environment by offering the opportunity to work with internationally renowned experts in modern AI and collaborate between different ELIZA sites in Germany, with industry, as well as with ELLIS units abroad.

ELIZA builds on a tightly woven European research network of 36 ELLIS units and over 300 ELLIS fellows and scholars, all internationally peer-reviewed for scientific excellence in machine learning-driven AI.

ELIZA links the research and educational activities across the participating institutions and collaborating companies through

  1. cross-site co-supervision setups for ELIZA PhD and Master’s students,
  2. cross-listed Master’s-level courses as well as joint courses across sites including through the BMBF-funded KI-Campus platform, and
  3. joint research and educational events such as thematic workshops and summer schools.

Industrial fellows actively collaborate with ELIZA students and academic fellows, can contribute to the teaching activities, and are given the opportunity to provide their industry perspective in the form of Konrad Zuse Lectures.

ELIZA leverages the support of the institutions behind the participating ELLIS units as well as the visibility of the ELLIS network in politics, economy, and society. Moreover, we link Germany’s top institutions in machine learning-driven AI into a sustained research and education platform with high international visibility and impact.

Target group:

  • Outstanding PhD candidates during their Master’s phase who come in with a Bachelor’s degree

Aim:

  • Fast-track PhD program (formal or de facto, depending on the institution)

Arrangements:

  • Scholarship holders will be integrated into the research group of an ELIZA fellow already during their Master’s phase. Active participation in research during entire Master’s
  • Thesis advised by an ELIZA fellow (academic or industrial)
  • Optional (but encouraged): Co-supervision by a 2nd ELIZA fellow at a different site
  • Scholarship holders participate in the ELIZA curriculum
  • Are eligible to take cross-listed Master’s level courses from other sites (regular course credit)
  • Fast-track PhD and/or Master’s regulations of hosting institution need to be followed

Funding:

  • Following official DAAD rates
  • Supplements for married candidates & for children
  • Relocation support: 950€ on average (specific amount depends on need)
  • Duration:
    • Up to Master’s completion or at most 2 years, whichever is earlier
    • Default should be a full 2-year funding period, but can be shorter in exceptional cases

Admissions:

  • Key acceptance criteria: outstanding aptitude for PhD studies in ML-driven AI based on background in AI and other relevant fields, Bachelor’s grades, research experience, diversity including gender balance and internationality, motivation, and topical match to hosting ELIZA fellows and ELIZA’s four research focus areas
  • Applications will be reviewed, and candidates optionally interviewed by the potential hosting ELIZA fellows, who will make a recommendation to the Scholarship Admissions Committee
  • Fast-track PhD and/or Master’s admission of hosting institution needs to be followed

Application process:

Currently, interested candidates should either

  • apply through the ELLIS PhD program (https://ellis.eu/phd-postdoc; the form will allow you state that you do not have a Masters degree) to work with one of the ELIZA academic fellows or
  • consult the webpages of ELIZA's academic fellows

Target group:

  • Supporting highly qualified Master’s students from underrepresented groups
  • Considering all areas of underrepresentation: e.g., gender, sexual orientation, nationality

Aim:

  • Increasing diversity in AI
  • Supporting students for whom it would be difficult to study (in Germany)

Arrangements:

  • Optional: Scholarship holders can be integrated into the research group of an ELIZA fellow already during their Master’s phase
  • Thesis advised by an ELIZA fellow (academic or industrial)
  • Optional (but encouraged): Co-supervision by a 2nd ELIZA fellow at a different site
  • Scholarship holders participate in the ELIZA curriculum
  • Are eligible to take cross-listed Master’s level courses from other sites (regular course credit)
  • Master’s regulations of hosting institution need to be followed

Funding:

  • Following official DAAD rates
  • Supplements for married candidates & for children
  • Relocation support: 950€ on average (specific amount depends on need)
  • Duration:
    • Up to Master’s completion or at most 2 years, whichever is earlier
    • Default should be a full 2-year funding period, but can be shorter in exceptional cases

Admissions:

  • Only students from underrepresented groups are eligible
  • Key acceptance criteria: outstanding aptitude for Master’s studies in ML-driven AI based on Bachelor’s grades, diversity in terms of gender and background (including internationality), motivation, as well as topical fit with the four research focus areas of ELIZA
  • Applications will be reviewed, and candidates optionally interviewed by the potential hosting ELIZA fellows, who will make a recommendation to the Scholarship Admissions Committee
  • Master’s admission of hosting institution needs to be followed

Application process:

Interested candidates who have already been accepted for Master's studies in AI at one of ELIZA's partner universities should inquire through the academic fellow with whom they would like to work.

Research Stay for ELLIS PhD Students based at German sites

 

Target group:

  • ELLIS PhD students at an ELIZA site

Aim:

  • Supporting 6-12 months research stays at an ELLIS unit outside of Germany

Arrangements:

  • Visiting existing co-supervisor at an ELLIS unit abroad for joint research

Funding:

  • Following official DAAD rates
  • Supplements for married students & for children
  • Mobility support: 766€

Duration:

  • Between 6 and 12 months

Funding decisions:

  • Each ELIZA site collects requests for funding with 2 yearly deadlines
  • Preference will be given to (1) ELIZA PhD students and to (2) ELLIS PhD students who are associated with ELIZA

Application process:

  • ELLIS PhD students at ELIZA sites should inquire through their PhD advisor.
 

Research Stay for ELLIS PhD Students from ELLIS Units abroad

 

Target group:

  • ELLIS PhD students at an ELLIS unit outside of Germany

Aim:

  • Supporting 6-12 months research stays at an ELIZA site
  • Competitive Konrad Zuse Visiting ELLIS PhD Student Scholarships
  • Attract top ELLIS students to Germany for their research mobility

Arrangements:

  • Visiting existing co-supervisor at an ELIZA site

Funding:

  • Following official DAAD rates
  • Supplements for married students & for children
  • Mobility support: 766€

Duration:

  • Between 6 and 12 months

Funding decisions:

  • Key acceptance criteria: outstanding aptitude for PhD studies in ML-driven AI based on previous research, topical match with the hosting ELIZA fellow as well as contribution to ELIZA’s four research focus areas
  • Applications will be reviewed in a lightweight process and candidates optionally interviewed by an ELIZA fellow, who will make a recommendation to the Scholarship Admissions Committee

Application process:

  • ELLIS PhD students wishing to visit an ELIZA site should inquire through the hosting ELIZA academic fellow.

Target group:

  • Outstanding candidates for a PhD in ELIZA

Aim:

  • Attract outstanding students through attractive co-supervision arrangement

Arrangements:

  • Mandatory co-supervision by an ELIZA fellow (academic or industrial) at a different ELIZA site
    • Close co-supervision with regular interaction, supervision agreement
    • Interactions at least every 2 months, 2 physical visits per year
    • 6-12 months research stay at co-supervisor’s ELIZA site
  • Mandatory participation in ELIZA curriculum
  • Doctoral regulations of the degree-granting institution apply as usual

Funding:

  • 100% E13 position following the institution’s pay grade
  • Relocation support: up to 766€

Duration:

  • Initial contract is expected to be at least 3 years

Admissions:

  • Key acceptance criteria: outstanding aptitude for PhD studies in ML-driven AI based on background in AI and other relevant fields, Bachelor’s and Master’s grades, research experience, motivation, diversity, and topical match to prospective ELIZA supervisor and co-supervisor as well as to ELIZA’s four research focus areas
  • Applications will be reviewed, and candidates interviewed by both prospective supervisors, who will make a recommendation to the PhD admissions committee
  • Doctoral admissions process of the degree-granting institution applies as usual

Application process:

Currently, interested candidates should either

  • apply through the ELLIS PhD program (https://ellis.eu/phd-postdoc) to work with one of the ELIZA academic fellows or
  • consult the webpages of ELIZA's academic fellows

Collaborations with industry in both research and education are a key component of the ELIZA School. To that end, ELIZA will maintain a broad set of industry collaborations, ranging from German AI startups over large German companies to multinational corporations. Our current partners include:

Aignostics

Aleph Alpha

Amazon

BioRN

Bosch / Bosch Center for Artificial Intelligence

Cellzome

Facebook

Google

Intel

Merantix

Mercedes-Benz

Siemens

Swift Management

Toyota Motor Europe

Valeo.AI

Volkswagen


ELIZA's industrial fellows are first-rate industrial AI researchers, who not only contribute their industrial perspective, but actively advance the state-of-the-art in machine learning-driven AI and its applications through scientific publications in premier publication venues.

The following table gives an overview of the academic ELIZA fellows, their respective ELIZA site and their academic institution(s).

Name ELIZA site Academic institution
Iryna Gurevych Darmstadt Technische Universität Darmstadt
Kristian Kersting Darmstadt Technische Universität Darmstadt
Heinz Koeppl Darmstadt Technische Universität Darmstadt
Mira Mezini Darmstadt Technische Universität Darmstadt
Jan Peters Darmstadt Technische Universität Darmstadt
Stefan Roth Darmstadt Technische Universität Darmstadt
Constantin Rothkopf Darmstadt Technische Universität Darmstadt
Begüm Demir Berlin Technische Universität Berlin & BIFOLD
Volker Markl Berlin Technische Universität Berlin & BIFOLD
Grégoire Montavon Berlin Technische Universität Berlin & BIFOLD
Klaus-Robert Müller Berlin Technische Universität Berlin & BIFOLD
Frank Noé Berlin Freie Universität Berlin & BIFOLD
Wojciech Samek Berlin Fraunhofer Heinrich-Hertz-Institut & BIFOLD
Marc Toussaint Berlin Technische Universität Berlin
Armin Biere Freiburg Universität Freiburg
Joschka Bödecker Freiburg Universität Freiburg
Thomas Brox Freiburg Universität Freiburg
Frank Hutter Freiburg Universität Freiburg
Abhinav Valada Freiburg Universität Freiburg
Fred Hamprecht Heidelberg Universität Heidelberg
Ullrich Köthe Heidelberg Universität Heidelberg
Anna Kreshuk Heidelberg European Molecular Biology Laboratory
Lena Maier-Hein Heidelberg Deutsches Krebsforschungszentrum
Tilman Plehn Heidelberg Universität Heidelberg
Carsten Rother Heidelberg Universität Heidelberg
Oliver Stegle Heidelberg Deutsches Krebsforschungszentrum & European Molecular Biology Laboratory
Daniel Cremers München Technische Universität München
Massimo Fornasier München Technische Universität München
Laura Leal-Taixé München Technische Universität München
Julia Schnabel München Technische Universität München & Helmholtz Zentrum München
Fabian Theis München Technische Universität München & Helmholtz Zentrum München
Xiaoxiang Zhu München Technische Universität München & Deutsches Zentrum für Luft- und Raumfahrt
Vera Demberg Saarbrücken Universität des Saarlandes
Manuel Gomez Rodriguez Saarbrücken Max-Planck-Institut für Softwaresysteme
Krishna Gummadi Saarbrücken Max-Planck-Institut für Softwaresysteme
Bernt Schiele Saarbrücken Max-Planck-Institut für Informatik
Christian Theobalt Saarbrücken Max-Planck-Institut für Informatik
Isabel Valera Saarbrücken Universität des Saarlandes
Zeynep Akata Tübingen Universität Tübingen
Matthias Bethge Tübingen Universität Tübingen
Michael Black Tübingen Max-Planck-Institut für Intelligente Systeme
Andreas Geiger Tübingen Universität Tübingen
Matthias Hein Tübingen Universität Tübingen
Philipp Hennig Tübingen Universität Tübingen
Ulrike von Luxburg Tübingen Universität Tübingen
Gerard Pons-Moll Tübingen Universität Tübingen
Bernhard Schölkopf Tübingen Max-Planck-Institut für Intelligente Systeme
Bob Williamson Tübingen Universität Tübingen