Technical Students for Next Generation Trigger Project 2026

CERN European Organization for Nuclear Research

Geneva

Key information

Publication date:06 January 2026
Workload:100%
Place of work:Geneva

Job summary

Join CERN and contribute to groundbreaking machine learning projects! Experience a dynamic work environment with endless learning opportunities.

Tasks

Explore continual learning approaches for trigger algorithms.
Develop machine learning solutions for the CMS trigger system.
Collaborate on MLOps and Python programming tasks.

Skills

Currently studying Applied Physics, Engineering Physics or related.
Strong understanding of applied physics fundamentals.
Good communication skills in a multicultural team.

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Job Description

What you'll do

In the coming upgrade to the Trigger system of the CMS Experiment many machine learning algorithms are being developed which will use low-level detector information to make decisions on whether to keep data or not. These algorithms will be exposed to a dynamic inference environment that will not only differ from their training environments but evolve over time as the detector conditions change. In the context of the Next Generation Trigger project you will explore continual learning approaches to incrementally updating trigger algorithms with new data specifically focusing on decision tree-based algorithms. The project will explore changing environments in the CMS detector as well as designing novel ways of training and updating decision trees. There will be opportunity to explore the implementation of these continual learning training techniques within the wider CMS trigger ML training infrastructure.

Your responsibilities

Develop an understanding of implementing machine learning in dynamic environments, including the impact on output quality and methods to mitigate these effects.

Acquire experience in developing and applying machine learning for the CMS trigger system, including fast machine learning and the techniques required to design trigger algorithms.

Acquire experience in Python programming and code development within larger teams, including machine learning operations (MLOps) and ML infrastructure.

Your profile

You are currently studying in Applied Physics, Engineering Physics or a related field.

You are required or encouraged by your university to complete a practical training period.

You are eager to develop your skills through on-the-job learning.

You enjoy teamwork and learning from others in a collaborative setting.

Your skills

A good understanding of applied physics fundamentals relevant to your studies.

Curiosity, precision and a proactive attitude towards learning.

Good communication skills and ability to work effectively in a multicultural team.

Additional Information

This call is open to nationals of a CERN Member or Associate Member State and nationals of Non-Member States from Institutes in the LHC experiments collaborations.

Application Deadline: 26/01/26 at 23:59 (Geneva time)

Employment Conditions:

Ideal Start Date: 01/05/2026

Weekly Working Hours: 40

Contract duration (in months): 4 to 12

Job Flexibility: Fully Onsite

Global Benefits

A monthly allowance of 3452 Swiss Francs (tax free).

30 days of paid leave per year plus 2 weeks annual closure.

Coverage by CERN’s comprehensive Health Insurance scheme (the contribution will be automatically deducted from your allowance).

Family supplement to subsistence allowance, depending on your individual circumstances.

Travel expenses to Geneva upon joining CERN, depending on your individual circumstances.

Be sure to meet the eligibility criteria

You are a national of a CERN Member State or Associate Member State .

You have completed at least 18 months of your studies (3 semesters) in the respective field at the time of the selection. PhD students or holders are not eligible.

You have a good knowledge of English or French.

You remain enrolled as a full-time student during your studentship.

You will need these documents to complete your application

Your CV (English or French)

Any document you consider relevant to your application

An academic reference letter (optional)

An academic transcript (optional)

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