SW Developer / Experimental Physicist (EP-ATL-OSW-2026-121-GRAP) — CERN
CHF 60'500 - 91'500
CERN · Geneva, GENEVA, Switzerland (GE)
- Location
- Geneva
- Contract
- full-time
- Posted
- 9 days ago
SalaryCHF 60'500 - 91'500
Role overview
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. of CERN
- 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.
- 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.
- Understanding of tracking challenges in high track density environments, such as at the High-Luminosity LHC.
Main responsibilities
- 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.
Key requirements
- Understanding of tracking challenges in high track density environments, such as at the High-Luminosity LHC.
Application process
- 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.
- 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.
Company and context
- 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-OSW-2026-121-GRAP 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 CERN’s 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.
Additional details
- 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.
- Ability to lead teams and define directions.
- Spoken and written English, with a commitment to learn French. Eligibility criteria:
- Job reference: EP-ATL-OSW-2026-121-GRAP Field of work: Experimental Physics Benchmark job: 200140 Applied Physicist Global Benefits
Notes and original content
- Your profile:
- Spoken and written English, with a commitment to learn French.
- Eligibility criteria:
- Job reference: EP-ATL-OSW-2026-121-GRAP Field of work: Experimental Physics Benchmark job: 200140
- Applied Physicist Global Benefits
- Overview of CERN
