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Lisbon Machine Learning School (LxMLS) 2026 -- CALL FOR MONITORS
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The Lisbon Machine Learning School (LxMLS) is a one-week summer school
taking place in Lisbon, Portugal, and focused on Natural Language
Processing (NLP) and Machine Learning. The school hosts around 200
students selected from more than 500 applicants from around the world
and enjoys top lecturers in machine learning from academia and
industry.

We are looking for candidates to take following responsibilities

- Master the LxMLS lab guide and code. See last year's guide as reference
  http://lxmls.it.pt/2025/documents/LxMLS_guide_2025.pdf

- Tutor students during the labs sessions explaining exercises and solving the
  student's doubts.

- Assist in the organization of the school including Q&As and organization of
  social activities

The ideal profile for this role is former LxMLS alumni around the
world that would like to repeat the experience but want to keep
learning ML and NLP. If you have extensive Python and ML experience
and feel like you could teach the guide, this can also be a good role
for you. We organize the team with Git/GitHub so experience with both
is a plus.

You will be able to attend morning and afternoon lectures for free.
With the exception of the lab sessions, monitors rotate in activities
so that there is available free time during the school.

Interested candidates should send an email with subject "Monitor
Application" to ramon@astudillo.com with a Curriculum Vitae as well as
following information

- Brief description of why you want to join
- Indication of whether you have attended any past edition of LxMLS as student
- Indication of whether you need to apply to the travel and lodging grants

Previous years monitors only need to send a CV and indicate if they
are applying to the travel and lodging grants an/or the core-member
grant

Important Dates
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* Monitor Application Deadline: May 22, 2026
* Notification of Admission: May 25, 2026
* (Registration is automatic for accepted monitors)
* Summer School: July 20 - 25, 2026 (arriving a day before is better)

Check this years details at https://bgmartins.github.io/lxmls-website-2026/

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