Foundation models for the hierarchical representation and reconstruction of particle collision events

Precision measurements at the Large Hadron Collider (LHC) require a detailed understanding of the particles produced in each collision and the relationships between them. However, reconstructing the underlying physics process remains extremely challenging. Different decay chains can produce similar experimental signatures, several particle assignments may be compatible with the same event, and undetected particles leave incomplete information. A representation connecting these observations to different possible physical interpretations could improve reconstruction and provide a common basis for multiple analyses.
In this project, we propose to develop a hierarchical representation of collision events within the HEP Foundry programme led by the supervisors. This representation will connect reconstructed particles, composite objects, and possible decay chains within a unified machine-learning model. The model will first be trained on a diverse range of simulated processes and then adapted to specific reconstruction tasks. The objective is to retain several physically compatible interpretations of an event and quantify their uncertainties. Higgs boson pair production (HH), particularly in the four-bottom-quark final state, will serve as the main physics case study. This process probes the Higgs self-coupling and the shape of the Higgs potential, making it a major objective of the LHC physics programme. Its challenging reconstruction and large backgrounds will provide a stringent test of the proposed framework.
This PhD project will naturally fit within the ScienceFace initiative, notably by contributing to the development of shared software tools for scientific data curation and the construction of scientific foundation models.

Model-Based Systems Engineering approach for the automation and optimization of qubit control architecture exploration

Designing the system architecture to control millions of qubits in a quantum computer is a complex challenge, driven by strict constraints spanning heterogeneous technical domains (microelectronics, quantum information, thermics). Addressing this complexity requires dedicated formalisms and tools to enable automated design and optimization. Where traditional approaches remain siloed, Model-Based Systems Engineering (MBSE) and the SysML v2 standard modeling language provide a unique integration framework for analyzing and exploring complex multi-physics design spaces.
To capture these architectures within SysML v2 models, this research aims to adapt this modeling language to the quantum domain by developing dedicated extensions (libraries and metadata).
Based on these models, this work will explore how reference quantum or hybrid algorithms impact these architectures, particularly through resource estimation for these applications and the applied error-correction mechanisms.
The PhD thesis intends to develop a methodology for automating and optimizing architectural exploration. To this end, it will investigate the adaptation of operational research algorithms for design space exploration, incorporating sensitivity analyses, constraint relaxation heuristics, and physical uncertainties.
This research aims to identify the most influential parameters and provide new insights capable of guiding efforts toward the most promising R&D avenues.
The thesis will be carried out at CEA Grenoble.