GPU&MLDeveloperforReconstructionandSimulation(EP-ALI-SC-2026-106-GRAP)

ALICE is pioneering the use of GPUs in Run 3 for the online processing and partly for offline reconstruction. To better leverage available GPU compute resources and improve reconstruction performance, we aim to investigate the use of machine learning. As a GPU and ML software developer, you will maintain, develop, and commission machine-learning-based GPU event reconstruction code for the ALICE experiment, in particular ML-based and ML-supported clusterisation, and track seeding in the ALICE TPC. In parallel, you will contribute to ALICE's Monte Carlo production ecosystem and simulation frameworks, focusing on workflow optimisation. This includes the full MC production infrastructure, simulation frameworks, automation of production, validation and integration of ML and GPU-code, and the development and use of intelligent computing tools across the ALICE computing chain. Your responsibilities * Commission the GPU TPC ML clusterisation as the default clusterisation code for data taking and for simulation. * Benchmark and improve the ML-based clusterisation in terms of processing performance and physics quality. * Investigate extending ML usage, including to TPC track seeding. * Contribute to the Monte Carlo production ecosystem, including workflow scheduling, multi-timeframe processing, multi-threading, and integration of ML/GPU components. * Develop and operate automated solutions for MC production, job orchestration, and validation, including ML-based anomaly detection. * Track the activities in the optimisation and modernisation of simulation and reconstruction frameworks (e.g. Geant, AliceO2), including ML-driven acceleration and GPU-based approaches. * Investigate components and algorithms of the ALICE computing chain (simulation, reconstruction, etc.) that could benefit from machine learning and develop prototypes. Your profile * Experience with high energy physics (HEP) experiments event reconstruction code (e.g. clusterisation or tracking). * Experience with G