Postdoctoral researcher (M/F) in particle physics

CNRS; France; Sur place; EUR 40 000 - 50 000; Plein temps

Résumé du poste

A prestigious research laboratory in France is seeking a postdoctoral researcher to contribute to the development of novel real-time trigger reconstruction architectures using deep learning for future particle collider experiments. The ideal candidate should hold a doctorate in particle physics and have experience in C++ and Python programming, as well as in machine learning models. The position involves collaboration with CEA/IRFU and CERN, focusing on advanced high-granularity calorimeter simulations.

Qualifications

• Holding a doctoral degree in particle physics.
• Experience in C++ and Python programming is desired.
• Experience in training and using Machine Learning models is desired.
• Experience with detector (Geant4) and electronics simulation is beneficial.
• Experience with FPGA firmware development and familiarity with trigger systems is beneficial.

Responsabilités

• Contribute to the simulation of a high-granular calorimeter with Geant4.
• Develop and optimize deep learning models for a distributed trigger system.

Connaissances

C++ programming

Python programming

Machine Learning models

Geant4 simulation

FPGA firmware development

Formation

Doctoral degree in particle physics

Description du poste

Offer Description

The Laboratoire Leprince‑Ringuet (LLR) is inviting applications for a two‑years postdoctoral position within the CMS group, in the context of the "DRIFTS" project funded by the ANR. DRIFTS is a collaborative project between CEA/IRFU, CERN and LLR. It aims to demonstrate novel real‑time trigger reconstruction architectures for future particle collider experiments, based on deep learning models distributed across multiple hardware processing stages. The mission of this position, based at LLR, is to contribute to these developments in collaboration with the various project stakeholders. The future high‑granularity endcap calorimeter of CMS (HGCAL) will serve as a platform to evaluate these developments using existing hardware components.

Responsibilities

• Contribute to the simulation of a high-granular calorimeter with Geant4 as well as its readout and trigger electronic chain.
• Develop and optimize deep learning models, trained for the reconstruction of events generated with this simulation framework and targeting their application on a distributed trigger system based on several processing stages.

The LLR is a laboratory of the CNRS/IN2P3 and the École Polytechnique, one of the grandes écoles of the Institut Polytechnique de Paris (IPP) specialized in science and engineering. The LLR is dedicated to fundamental research in high-energy particle physics and astroparticle physics. Its members work on the LHCb and CMS experiments at the LHC, the T2K, Super‑K and Hyper‑K neutrino experiments in Japan, and in FERMI, HESS and CTA for gamma‑ray astronomy. The CMS group of LLR is a founding member of the CMS Collaboration and is composed of about 30 members. It has designed, built, and is responsible for the operation of the Level‑1 (L1) trigger for the electromagnetic calorimeter (ECAL). It has also designed the calorimeter mechanics and contributed to the front‑end readout electronics. The group is strongly involved in the development of the future high‑granularity endcap calorimeter (HGCAL) for the high‑luminosity phase of the LHC, in particular on its mechanical design, on the generation of the L1 trigger primitives, and on the development of offline reconstruction algorithms. The position will be based at the LLR in Palaiseau (near Paris) with close collaboration with colleagues from CEA/IRFU and from CERN.

Qualifications

• Holding a doctoral degree in particle physics.
• Experience in C++ and Python programming is desired.
• Experience in training and using Machine Learning models is desired.
• Experience with detector (Geant4) and electronics simulation would be beneficial.
• Experience with FPGA firmware development and familiarity with trigger systems would be beneficial.