Event Reconstruction Physicist (EP-FCC-2026-166-GRAP)

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.

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.

Requirements

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.; C++20.; Clustering, event reconstruction.; Detector simulation.; Particle flow.; CMSSW,Key4hep.; Heterogeneous computing.; Spoken and written English, with a commitment to learn French.; 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.

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.