Your responsibilities
The Event Filter (EF) is part of the ATLAS Trigger and Data Acquisition (TDAQ) system and will consist of a multi-threaded asynchronous processing farm of commodity servers (including CPUs with or without accelerators) running a subset of offline-like reconstruction algorithms and menu-driven event selection. It processes event data from the Dataflow system and provides the accept/reject decision and selected reconstruction data for offline storage. The high luminosity (and consequently high pile-up) conditions expected in Phase-II give rise to specific challenges for the object and event reconstruction algorithms planned for the EF.
ATLAS plans to develop the most effective and performant solution for the EF tracking and for EF muon reconstruction and deploy them on the most suitable hardware architecture employing standard numerical and Machine Learning models.
The selected candidate will be part of the CERN ATLAS Data Processing team. He or she will either take on responsibilities in the development and deployment of EF tracking solutions. After studying the existing tracking pipelines, the selected candidate will initially focus on optimising them and providing an implementation of such pipelines on accelerators. Physics and computation performance evaluations of the studied configurations will be provided. Then, the selected candidate will continue to optimise the chosen solution and prepare it for final deployment.
Another activity area is the development and deployment of EF muon reconstruction approaches, with focus on developing a reconstruction chain within the ACTS tracking framework and/or developing machine learning approaches for muon reconstruction. The candidate will also investigate deploying the algorithms on systems with coprocessors and on providing performance evaluations of the configuration studied.
Up to two positions will be filled as part of the Next Generation Trigger programme (https://cern.ch/nextgen-proposal).
Your profile
Skills and/or knowledge
Experience with modern C++ programming techniques, preferably with experience with offloading to hardware accelerators.Knowledge of hardware accelerators; preferably with experience in GPU coding in CUDA or FPGA firmware development in HLS, System Verilog, or VHDL as a plus.Competence with developing track / muon reconstruction software and / or developing, training and evaluating performance of ML-based algorithms.Knowledge of track reconstruction concepts in HEP experiments.Proficiency with software development tools in a Linux environment is a requirement.Eligibility criteria:
You are a national of a CERN Member or Associate Member State.Job closing date: 10.04.2024 at 12:00 AM (midnight) CET.
Job reference: EP-ADP-OS-2024-52-GRAP
Contract duration: 24 months, with a possible extension up to 36 months maximum.
Target start date: 01-May-2024
This position requires:
Stand-by duty, when required by the needs of the Organization.Work during nights, Sundays and official holidays, when required by the needs of the Organization.What we offer
A monthly stipend ranging between 6194 and 6808 Swiss Francs per month (net of tax).Coverage by CERN's comprehensive health scheme (for yourself, your spouse and children), and membership of the CERN Pension Fund.Depending on your individual circumstances: installation grant; family, child and infant allowances; payment of travel expenses at the beginning and end of contract.30 days of paid leave per year.On-the-job and formal training at CERN as well as in-house language courses for English and/or French.About us
At CERN, the European Organization for Nuclear Research, physicists and engineers are probing the fundamental structure of the universe. Using the world's largest and most complex scientific instruments, they study the basic constituents of matter - fundamental particles that are made to collide together at close to the speed of light. The process gives physicists clues about how particles interact, and provides insights into the fundamental laws of nature. Find out more on http://home.cern.
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