Machine-learning in Secondary Emission Monitor (SEM) optimization

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