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ELIZA

What is ELIZA?

ELIZA (Excellence in Learning and Intelligent Systems) builds upon ELLIS (European Laboratory for Learning and Intelligent Systems), the leading academic network for machine learning-focused AI in Europe. The ELIZA program connects seven competitively selected German ELLIS Units (Berlin, Darmstadt, Freiburg, Heidelberg, Munich, Saarbrücken, Tübingen) including their academic institutions and industry partners. The goal is to be the leading German brand for research and research training in machine learning-driven AI. The ELIZA graduate school offers students a combination of excellent, research-based education at the Master’s and doctoral levels, supervision provided by internationally renowned mentors from both academia and industry, and networking opportunities across different sites. More information about ELIZA can also be found at www.eliza.school.

For details on the ELIZA Master’s scholarship, scroll down.

Research Topics

The main goal of ELIZA is to achieve scientific and educational excellence in learning and intelligent systems covering the following topics:

  1. Foundations of AI including learning-driven disciplines such as robot learning, computer vision, natural language processing, etc.
  2. Applications in autonomous systems
  3. Machine learning for all areas of science from life science to physics

The ELIZA Network

The ELIZA network spans seven ELLIS units across Germany, comprising both academic institutions and their industry partners.

Networking ELIZA with ELLIS

ELLIS is a highly visible European grassroots initiative for modern AI that the ELIZA program leverages 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. The vision of ELIZA is to build an outstanding foundation within ELLIS for research excellence in machine learning-driven AI. Specifically, ELIZA aims 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.

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, ELIZA links Germany’s top institutions in machine learning-driven AI into a sustained research and education platform with high international visibility and impact.

Academic Fellows at the Freiburg Unit

List of the academic ELIZA fellows at the University of Freiburg:

ELIZA Funding Possibilities

ELIZA Master’s Scholarship

Application deadline: 30.04.2024

ELIZA offers funding possibilities for Master’s students that pursue a degree in AI-related fields in Freiburg. There are two tracks targeting different candidates as listed below.

Who is eligible?

Track 1: Research-Oriented Master’s Scholarships for Students Targeting an AI-Related PhD

  • 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
  • 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 need to be followed

Track 2: Konrad Zuse Master’s in AI Scholarships for Students from Underrepresented Groups

  • 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
  • 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 the University of Freiburg need to be followed

What are the benefits?

  • ELIZA scholarships follow official DAAD rates
  • Relocation support
  • Duration up to master’s completion (at most 2 years) with periodic reviews

How to apply?

Email the following documents to eliza@cs.uni-freiburg.de:

  • CV
  • Certificates and transcripts of BSc and MSc (if started)
  • Research statement
  • Statement of how you would like to contribute to the ELIZA/ELLIS Freiburg unit

Applications will be reviewed, and candidates optionally interviewed by the Freiburg ELIZA fellows, who will make a recommendation to the Scholarship Admissions Committee

Opportunities for ELLIS Students

ELIZA offers funding possibilities for ELLIS PhD Students based at ELIZA sites that visit ELLIS units outside of Germany and for ELLIS PhD students based at ELLIS units abroad visiting an ELIZA site.

Who is eligible?

  • Track 1: ELLIS PhD students at an ELIZA site visiting an ELLIS unit outside of Germany
  • Track 2: Incoming ELLIS PhD students from an ELLIS unit outside of Germany

What are the benefits?

  • Financial support for 6-12 month research stays
  • ELIZA scholarships follow official DAAD rates
  • Relocation support

How to apply?

Track 1: ELLIS PhD students at ELIZA sites should inquire through their PhD advisor

  • 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

Track 2: Incoming ELLIS PhD students should inquire through the hosting ELIZA academic fellow

  • 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 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

ELIZA PhD Program (fully funded PhD positions)

We offer fully funded PhD positions at the Freiburg unit that are part of the ELIZA PhD program.

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

Duration

  • The initial contract is expected to be at least 3 years

Admission

  • 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 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