SW Developer Experimental Physicist (EP-ATL-OSWGRAP)

The Event Filter (EF) is part of the ATLAS Trigger and Data Acquisition (TDAQ) system and consists of a multi-threaded asynchronous processing farm of commodity servers (CPUs with or without accelerators) running a subset of offline-like reconstruction algorithms together with menu-driven event selection.

The high-luminosity conditions expected during Phase-II operations introduce significant challenges for object and event reconstruction algorithms planned for the EF particularly for track reconstruction. The recent definition of the EF farm as a heterogeneous architecture combining CPUs and GPUs opens new opportunities for deploying machine learning models within the EF tracking workflow.

You will be part of the CERN ATLAS team and will contribute to research into the application of ML techniques for track reconstruction at the HL-LHC with the goal of identifying and exploring the most promising approaches for deployment in the ATLAS EF tracking. The position is part of the Next Generation Trigger programme.

Your responsibilities:

• Conduct research on machine learning (ML) and AI-based approaches for track reconstruction with a focus on the applicability and performance of these methods in the high pile-up environment of the HL-LHC.
• Investigate and benchmark novel ML-based tracking algorithms and their integration into the ACTS-based EF tracking workflow.
• Contribute to studies of both physics performance and computational performance of the different configurations under study.
• This role includes team supervision responsibilities.

Your profile:

• Understanding of tracking challenges in high track density environments such as at the High-Luminosity LHC.
• Experience in the development and application of machine learning or deep learning methods in a physics or scientific computing context.
• Hands-on experience in the development of offline and/or online reconstruction software.
• Ability to lead teams and define directions.

Skills:

• Machine learning and deep learning frameworks.
• Experience with ML inference deployment.
• Knowledge of ML model training evaluation and optimisation including hyperparameter tuning and performance benchmarking.
• Programming languages: C and Python including software development workflows (Git Jira).
• Experience with large-scale scientific software frameworks (e.g. ACTS Athena) is considered an asset.
• Spoken and written English with a commitment to learn French.

Eligibility criteria:

• You have a professional background in PhD in Particle Physics / CS (or a related field) and have either:
  • a Masters 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.

Additional Information :

Job closing date: 17.07.2026 at 23:59 CEST.

Contract duration: 24 months with a possible extension up to 36 months maximum.

Working hours: 40 hours per week

Job flexibility: Fully Onsite

Target start date: 01-October-2026

This position involves:

• Stand-by duty when required by the needs of the Organization.
• Work during nights Sundays and official holidays when required by the needs of the Organization.

Job reference: EP-ATL-OSWGRAP

Field of work: Experimental Physics

Benchmark job: 200140 - Applied Physicist

Global Benefits

• A monthly stipend between 6372-7004 Swiss Francs per month (tax free) depending on your degree.
• 30 days of paid leave per year plus 2 weeks annual closure.
• Coverage by CERNs comprehensive health insurance scheme (for yourself your spouse and children) and membership of the CERN Pension Fund.
• Family child and infant monthly allowances depending on your individual circumstances.
• A relocation package (installation grant and travel expenses) depending on your individual circumstances.
• Possibility to extend your contract up to 36 months.
• On-the-job and formal training including language classes.

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Remote Work :

No

Employment Type :

Full-time