Job Description 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 hyper‑parameter 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 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.
Employment Details 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-OSW‑2026‑121‑GRAP
Global Benefits
A monthly stipend between 6 372‑7 004 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 Information Field of work: Experimental Physics
Benchmark job: 200140 - Applied Physicist
#J-18808-Ljbffr
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 hyper‑parameter 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 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.
Employment Details 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-OSW‑2026‑121‑GRAP
Global Benefits
A monthly stipend between 6 372‑7 004 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 Information Field of work: Experimental Physics
Benchmark job: 200140 - Applied Physicist
#J-18808-Ljbffr
SW Developer / Experimental Physicist (EP-ATL-OSW-2026-121-GRAP) Arbeitgeber: Physics World
CERN ist ein herausragender Arbeitgeber, der Ihnen die Möglichkeit bietet, an einem innovativen Projekt wie RADNEXT 2030 teilzunehmen, das den Zugang zu hochmodernen Strahlungstest-Einrichtungen in Europa koordiniert. Mit einem starken Fokus auf Mitarbeiterentwicklung und einer internationalen Arbeitsumgebung fördert CERN eine Kultur der Zusammenarbeit und des Wissensaustauschs, während Sie von attraktiven Vorteilen wie einem steuerfreien Gehalt, umfassender Gesundheitsversorgung und großzügigen Urlaubsregelungen profitieren. Hier haben Sie die Chance, Ihre Fähigkeiten in der angewandten Physik weiterzuentwickeln und aktiv zur Forschungsgemeinschaft beizutragen.
