Your responsibilities
We are seeking a passionate and motivated Engineer with a training in machine learning techniques to join our Efficient Particle Accelerators project team. In this role, you'll join the Accelerator Systems Department (SY), more specifically the Accelerator Beam Transfer Group (ABT), which is responsible for the design, development, construction, installation, exploitation and maintenance of injection and extraction related equipment and beam-transfer systems.
As a Junior Engineer, you will play a key role in the selection and deployment of an Internet of Things (IoT) electronic acquisition solution for collecting data from accelerator parts and developing the corresponding software stack to process the collected data using machine learning techniques for fault prognostics and enhanced diagnostic capabilities.
Specifically, you will:
Collaborate with a team of engineers to design, develop, and implement a hardware and software system for data collection and machine learning-based fault prognostics;Develop and implement algorithms for data acquisition, preprocessing, and feature extraction from the IoT device; Train and validate machine learning models for fault/anomaly detection, classification, and prognostics; Integrate machine learning models into the software stack for real-time fault detection and notification; Document and test the developed hardware and software components to ensure reliability and performance; Stay up-to-date with the latest advancements in machine learning and hardware technologies relevant to the project; Develop a framework to simplify the adoption of a similar software layer from other groups;Play a role in the coordination of activities within the Equipment Automation work-package and the community of scientists and engineers working on similar activities.Your profile
Skills and/or knowledge
Initial experience with python programming and data science that can be demonstrated by recent university projects, summer placements or an initial work experience.Project experience with machine learning techniques for anomaly detection will be considered a plus.Additionally, some hardware experience is desired at with electronics development platforms such as one or more of the following: rasberry Pi, digital signal processors boards, data acquisition systems, sensors or analog to digital converters.Eligibility criteria:
You are a national of a CERN Member or Associate Member State.By the application deadline, you have a maximum of two years of professional experience since graduation in Engineering (or a related field) and your highest educational qualification is either a Bachelor's or Master's degree.You have never had a CERN fellow or graduate contract before.Applicants without University degree are not eligible.Applicants with a PhD are not eligible.Job closing date: 14.04.2024 at 12:00 AM (midnight) CET.
Job reference: SY-ABT-PPE-2024-53-GRAE
Contract duration: 24 months, with a possible extension up to 36 months maximum.
Target start date: 01-July-2024
This position requires:
Work in Radiation Areas.Interventions in underground installations.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 5119 and 5631 Swiss Francs (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.
Diversity has been an integral part of CERN's mission since its foundation and is an established value of the Organization. Employing a diverse workforce is central to our success.