About us

At CERN, the European Organisation for Nuclear Research, physicists and engineers are probing the fundamental structure of the universe. Using the world's largest and most complex scientific instruments, they study the basic constituents of matter - fundamental particles that are made to collide together at close to the speed of light. The process gives physicists clues about how particles interact, and provides insights into the fundamental laws of nature. 

Job description

ALICE is pioneering the use of GPUs in Run 3 for the online processing and partly for offline reconstruction. To better leverage available GPU compute resources and improve reconstruction performance, we aim to investigate the use of machine learning.

As a GPU and ML software developer, you will maintain, develop, and commission machine-learning-based GPU event reconstruction code for the ALICE experiment, in particular ML-based and ML-supported clusterisation, and track seeding in the ALICE TPC.

In parallel, you will contribute to ALICE's Monte Carlo production ecosystem and simulation frameworks, focusing on workflow optimisation. This includes the full MC production infrastructure, simulation frameworks, automation of production, validation and integration of ML and GPU-code, and the development and use of intelligent computing tools across the ALICE computing chain.

Your responsibilities

• Commission the GPU TPC ML clusterisation as the default clusterisation code for data taking and for simulation.
• Benchmark and improve the ML-based clusterisation in terms of processing performance and physics quality.
• Investigate extending ML usage, including to TPC track seeding.
• Contribute to the Monte Carlo production ecosystem, including workflow scheduling, multi-timeframe processing, multi-threading, and integration of ML/GPU components.
• Develop and operate automated solutions for MC production, job orchestration, and validation, including ML-based anomaly detection.
• Track the activities in the optimisation and modernisation of simulation and reconstruction frameworks (e.g. Geant, AliceO2), including ML-driven acceleration and GPU-based approaches.
• Investigate components and algorithms of the ALICE computing chain (simulation, reconstruction, etc.) that could benefit from machine learning and develop prototypes.

Your profile

• Experience with high energy physics (HEP) experiments event reconstruction code (e.g. clusterisation or tracking).
• Experience with GPU programming and ML training and inference.
• Practical experience with debugging large distributed applications.

Skills:

• Strong knowledge of the C++ programming language on Linux.
• Knowledge of at least one GPU programming toolkit such as CUDA or HIP.
• Knowledge of an ML framework such as ONNXRuntime.
• Knowledge of debugging tools such as GDB and profiling tools such as perf.
• Ability to work in a team.
• Spoken and written English, with a commitment to learn French.

Eligibility criteria:

• You are a national of a CERN Member or Associate Member State.
• You have a professional background in Physics (or a related field) and have either: 
  
  • a Master's degree with 2 to 6 years of post-graduation professional experience;
  • or a PhD with no more than 3 years of post-graduation professional experience.
• You have never had a CERN fellow or graduate contract before.

This position involves:

• Participation in a regular stand-by duty, including nights, Sundays and official holidays.
• Stand-by duty, when required by the needs of the Organisation.

What we offer

• A monthly stipend ranging between 6,372-7,004 Swiss Francs (net of tax).
• Coverage by CERN's comprehensive health scheme (for yourself, your spouse and children), and membership of the CERN Pension Fund.
• Depending on your individual circumstances: installation grant; family, child and infant allowances; payment of travel expenses at the beginning and end of contract.
• 30 days of paid leave per year.
• On-the-job and formal training at CERN as well as in-house language courses for English and/or French.

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