1st Munich Legal Tech Summer School, 2-6 August 2021, Germany

Publish Date: Mar 15, 2021

Deadline: Apr 25, 2021

Event Dates: from Aug 02, 2021 12:00 to Aug 06, 2021 12:00


  • Interdisciplinary Legal Tech summer school for PhD students and early carreer Postdocs from Computer Science, Law, and adjacent disciplines
  • Munich, Germany and online; 2nd to 6th of August 2021
  • Application deadline: 25th of April 2021
  • The summer school will be a hybrid event allowing onsite and online participation. If local health guidelines allow, we will enable onsite participation in Munich under a comprehensive hygiene scheme. If you are not able to travel or do not feel safe in doing so, you can participate online.
  • The summer school is funded by Volkswagen Foundation


The digital revolution has democratised many aspects of our lives. Access to knowledge is no longer restricted to those who can afford 32 volumes of Encyclopædia Britannica or have access to university libraries, instead it is available to everyone with access to the internet. The access to the fine arts, but also to once expensive services like translation, was opened up to new classes of citizens by digitisation. For a long time, the legal domain was arguably one of the biggest resistance to digitisation efforts and in some aspects still struggles to catch up with other industries.

However, things are changing, not least because of a new generation of young researchers and professionals in both, law and computer science. The mission of the 1st Munich LegalTech Summer School is to bring together excellent junior researchers from both disciplines to foster an interdisciplinary exchange. With talks from world-class experts, collaborative practical lab sessions, and discussion sessions, we want to educate the next generation of LegalTech researchers. Thereby, we want to examine the different sub areas of the research, from a legal, as well as from a computer science point of view, with talks including the topics Natural Legal Language Processing, Court Case Outcome Prediction, Legal Data Science, and Legal Reasoning.

The focus of the summer school will be on empirical research. Therefore, an important part will be hands-on lab sessions where participants are encouraged to apply their newly acquired knowledge on real datasets and problems within interdisciplinary teams.

Session Formats

  • Talks: Each day will start with a lecture from an internationally renowned  expert.
  • Labs: In succession to most  talks there will be related Lab-Sessions in which participants will work and discuss in interdisciplinary teams of 2 – 4 students. They will apply knowledge gained in the talks by analyzing datasets, using tools, and discuss results.
  • Poster presentation: On the first day there will be a poster presentation where all participants can present their own research (interest) to the fellow participants.
  • Panel discussion: A panel of experts will discuss ethical implications of the usage of AI for law and answer questions from the participants.
  • Excursion: Participants will visit the Bavarian countryside and get the opportunity to network with speakers and participants.

Application and Costs

Participation in the Summer School will be free of charge. Travel and accommodation costs will be covered, thanks to generous funding from the Volkswagen Foundation. Application will close 25th of April 2021 and notifications will be sent out in the first weeks of May.

The number of participants is limited to 30 in order to provide an interactive learning atmosphere. We invite applications from PhD students and recently graduated postdocs from the fields of computer science, law, and related fields. In order to apply, please write an E-Mail including the following documents:

  • Motivation statement
  • CV
  • Documents showing you're enrolled as a PhD student / you recently finished your PhD and are now working as a postdoc
  • One page abstract of your dissertation topic or research interest

For more information click "LINK TO ORIGINAL" below.

Further Official Information

Link to Original

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