Applied Physicist (BE-CSS-DSB-2022-94-LD)

Company Description

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 .

Job Description

Introduction

Are you a curious applied physicist with experience in machine learning techniques for modelling and optimal control? Do you want to work in an international team building solutions to automate various aspects of accelerator control? Join CERN’s Controls Software & Services group, to work on AI for the largest particle physics laboratory in the world. Take part!

You will join the Beams Department, which provides CERN with the unique competencies required for the conception, design, survey, alignment, control and operation of accelerators, accelerator test facilities, secondary beam lines and experimental areas. It also invests heavily in R&D for cutting edge technology for robotics, high power computing and since recently data science.

You will be working within the Controls Software & Services group (CSS), Data Science for Beam Operation (DSB) section, responsible for designing and implementing numerical optimisation and ML algorithms to solve various to-date intractable problems, providing accurate, fast-executing, online models for various processes and implementing operational ML tools for CERN’s accelerator complex.

Functions

• Collaborate with the Operations and Experimental Areas Groups to provide and commission tools and algorithms to improve the accelerators and beam lines operations performance and efficiency.
• Collaborate with various CSS sections to provide ML frameworks and standards for the control room.
• Develop expertise in implementing digital twins and learned models for various accelerator processes.
• Define new research directions for sample-efficient and robust reinforcement learning for accelerator control.
• Supervise junior colleagues such as students and post-graduates.
• Contribute and share know-how in collaborations on Data Science topics such as AI with other CERN teams and external institutes.