Additionalinformation

Additional Information

Job closing date: 13.11.2026 at 23:59 CET.

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

Working hours: 40 hours per week

Job flexibility: Hybrid

Target start date: 01-January-2027

This position involves:

• Participation in a regular stand-by duty, including nights, Sundays and official holidays.
• Work during nights, Sundays and official holidays.
• Shift work.

Given the occupational health risks associated with this position, the selected candidate must obtain medical clearance before a contract offer is confirmed.

Job reference: EP-FCC-2026-166-GRAP

Field of work: Experimental Physics

Benchmark job: 200140 - Applied Physicist

Global Benefits

• A monthly net stipend of either 6372 or 7004 Swiss Francs per month 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.

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Companydescription

Company Description

Jobdescription

Job Description

The next generation of experiments, the HL-LHC upgrade of CMS and the Future Circular Collider, needs event reconstruction that is fast, accurate and portable across CPUs and GPUs. Every reconstruction algorithm and every machine-learning model is trained and judged against the simulation truth. Today that truth is stored in static, detector-specific objects. Each object is pre-grouped for one purpose, it cannot be navigated, and it separates calorimeter truth from tracker truth.

CERN has built a prototype that replaces them. It is one navigable, event-level graph of the generator and simulation history, with calorimeter and tracker hit indices on top. Reconstruction code then reasons in terms of stable physics abstractions: particles, vertices, decay branches and hits.

You will make this the truth model of the future. You will generalise the prototype into an experiment-independent library, you will bring it into the FCC software stack, and you will use it to build the next reconstruction: graph algorithms and modern machine learning for clustering, particle flow and particle identification, and reliable matching between reconstructed objects and truth. You will join the EP-FCC group, and you will work on both CMS and FCC-ee.

Your responsibilities:

• Generalise the CMS MC-truth graph into a detector-independent and experiment-independent library. Separate the graph data model, the navigation API and the hit index from the experiment data-format bindings.
• Develop the bindings. Keep the CMSSW binding working, and add a binding for the FCC stack (Key4hep, EDM4hep, Gaudi), so that both experiments build the same graph from their own simulation output.
• Extend the data model. Add multi-surface boundary crossings, sparse association storage, and a reduced hit-index layout. Handle pileup consistently.
• Develop the reconstruction-to-truth association. Extend the adaptive branch associator, which selects the truth level that best matches a reconstructed object, and validate it on merged topologies such as converted photons, bremsstrahlung and early hadronic interactions.
• Define what is reconstructible. Derive it from the truth graph and the detector description, using cell granularity, energy and time resolution, readout thresholds and two-object separation.
• Apply portable graph algorithms and advanced machine learning, for example graph neural networks and transformers, to reconstruction tasks in CMS (HGCAL and TICL) and in FCC detectors: clustering, linking, particle flow and particle identification.
• Build the training datasets and the labels from the truth graph. Define the metrics, calibrate the model outputs, and check the model before it feeds reconstruction.
• Contribute the code to the official repositories of CMS and of the FCC software stack, with unit tests, documentation and code review.
• Present the results in CMS and FCC meetings, in software forums and in publications.
• This role includes team supervision responsibilities.

Your profile:

• A Master's Degree or a PhD in Physics, Computer Science, Applied Mathematics or a related field.
• Demonstrated experience with modern C++ in a large, shared and peer-reviewed codebase, for example CMSSW, Gaudi or Key4hep.
• Demonstrated experience with time-aware event reconstruction and detector simulation in a high-energy physics experiment.
• Demonstrated experience with heterogeneous and portable computing, for example Alpaka, SYCL, CUDA or HIP.
• Experience with Python and with at least one machine-learning framework, preferably PyTorch.
• Experience with graph and clustering algorithms.
• Experience with git, code review and continuous integration.

Skills:

• C++20.
• Clustering, event reconstruction.
• Detector simulation.
• Particle flow.
• CMSSW,Key4hep.
• Heterogeneous computing.
• 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.

Qualifications