Machine-learning in Secondary Emission Monitor (SEM) optimization

Secondary Emission Monitors (SEMs) are used at accelerator facilities around
the world to characterize charged particle beams. You will work on the
optimization of monitor design, implementation, operation and in particular
image analysis.
Your work will include yield studies in simulation and experiments, aiming at
improving the absolute calibration, as well as the calibration stability of SEM
Foils used in the CERN Super Proton Synchrotron slow extraction lines.
The aim of the project will be to develop new strategies for online
calibration, compare SEM measurements with Optical Transition Radiation
(OTR)-based monitors and Cherenkov detectors, improve the overall monitor
design, including optimization of the electrodes/grids to enhance secondary
emission, and in particular enhance data analysis through the application of
machine learning techniques.