Student Sessions 2027

Europe/Zurich
40/S2-C01 - Salle Marie Sklodowska-Curie (CERN)

40/S2-C01 - Salle Marie Sklodowska-Curie

CERN

115
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Description

What? Your chance to give a lecture and present your project to fellow students and colleagues! Please note that we only have 30 places available.

When? From the 4th August to the 6th August 2026, 09:00 to 12:00 CEST

The Student Sessions are your chance to volunteer to give a talk on your work project at CERN - just like you have seen the lecturers do during the Summer Student Lecture programme.

For inspiration: Talks from 2025 Student Session

More details:

The talk will last for 10 minutes and will be followed by 5 minutes of questions. The session will be recorded and made available online. The titles of the presentations will be published in the CERN Annual Report next year as having been part of the Summer Student Lecture Programme.

There are only 30 places available (10 per day), and they are allocated by lottery.

Note that the presentation should not exceed 5-8 slides. Don't forget to email to our Summer Student Team a copy of your presentation as well as a brief autobiography by July 29th, 2026 so that we can introduce you.

DO NOT FORGET TO INVITE YOUR SUPERVISORS AND COLLEAGUES TO JOIN US! 

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      Student Session: Student Session - Day 1
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      Student Session: Student Session - Day 2
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        Please enter your presentation and a short description of your project

      • 09:45
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        Please enter your presentation and a short description of your project

          

        HGCAL is a key component of the next phase of the CMS upgrade. This study aims to monitor potential anomalies during HGCAL operation using machine learning methods. Due to the unique hexagonal layout of HGCAL, conventional convolutional neural network (CNN) algorithms are no longer directly applicable. I have innovatively applied the Radial Distribution Function (RDF) to address this challenge, achieving better results than traditional approaches. This work may offer new perspectives for future applications of machine learning in high energy physics

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      Student Session: Student Session - Day 3
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