Your responsibilities
The Large Hadron Collider (LHC) generates billions of particle collisions per second, with only a small fraction relevant for physics research. The CMS detector at LHC uses a multi-level trigger system, with the Level-1 Trigger (L1) implemented in FPGA devices, playing a crucial role in filtering and selecting relevant events for analysis.
To address the trade-off between generality and sensitivity in new physics searches, CMS is exploring unsupervised machine learning for anomaly detection. Implementing this at the L1 trigger level offers significant potential for expanding physics reach.
CMS has deployed two anomaly detection algorithms in its L1 trigger for 2024, and this project aims to develop an advanced anomaly detection system for the Phase 2 upgrade. The focus will be on using high-level synthesis languages, FPGAs, and SoCs to implement new ML models, including transformer models, which can improve robustness to noisy or imbalanced data. Your work will involve:
Implementing and optimizing firmware and software for ultra-low latency anomaly detection on FPGAs with CMS L1 trigger data.Designing and optimizing algorithms to maximize physics reach within FPGA resource limits.Evaluating state-of-the-art FPGA and SoC devices and their accelerator capabilities.Conducting feasibility studies on advanced anomaly detection techniques, including transformer models.Contributing to the development of CMS L1 trigger and scouting systems and supporting the integration of these systems with teams.Supervising summer and technical students.Presenting your work to CMS teams, Oracle collaborators, and at relevant conferences and workshops.Your profile
Skills
Significant experience with the design and simulation of FPGA-based electronics.Knowledge and application of programming techniques and languages, specifically C++ and Python.Knowledge of deep learning algorithms as used in particle physics experiments.Understanding of the physics objectives and underlying technology of particle physics detectors at the LHC.Ability to effectively communicate scientific and technical results in spoken and written English.Experience working on the low-level trigger of a particle detector experiment is advantageous.Experience with tools for implementing ML algorithms on FPGA devices is a plus.Eligibility criteria:
You are a national of a CERN Member or Associate Member State.You have a professional background in Applied 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.Job closing date: 12.12.2024 at 23:59 hrs CET.
Contract duration: 24 months, with a possible extension up to 36 months maximum.
Working hours: 40 hours per week
Target start date: 01-February-2025
This position involves:
Residence in the immediate vicinity of the Organization's installations.Job reference: EP-CMD-2024-202-GRAP
Field of work: Applied Physics
What we offer
A monthly stipend ranging between 6212 and 6828 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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