Dr. Eduard Simioni (Ph.D)

Senior Electronics Projects Engineer at Arktis Radiation Detectors

Dr. Eduard Simioni (Ph.D) has a diverse work experience beginning in 2005. Dr. Eduard started as a PhD in Physics at Nikhef (Nationaal instituut voor subatomaire fysica), where they conducted R&D of straw tube module for the LHCb Outer Tracker sub-detector, Quality Assurance and Verification tests, and discovered an unexpected ageing phenomena (OT Ageing). In 2010, they moved to Johannes Gutenberg University Mainz as a Postdoctoral Research Associate, where they used machine learning based technique and advanced data analysis on big data aimed at particle physics research (Higgs and Supersymmetry). Dr. Eduard also developed firmware for high speed links for Multi Gigabit transceivers in FPGAs, simulated PCB signal integrity and lab measurements. In 2014, they began working at CERN as a Technical Project Manager, where they coordinated the core teams of a international complex project in a matrix organization, maintained resources and budget plans, managed the firmware team and operation team, led software and simulation development, and trained on software best practice. In 2021, they moved to Advanced Sterilization Products (ASP) as a Data Science Specialist (temporary). Currently, they are a Senior Electronics Projects Engineer at Arktis Radiation Detectors.

Dr. Eduard Simioni has a diverse educational background. In 1994, they completed their High School Diploma at the Instituto Tecnico Industriale E. Fermi, Treviso in Industrial Chemistry. From 1999 to 2004, they obtained their Scientific Masters (MsC) in Physics from the Università degli Studi di Padova. In 2005, they completed their Doctor of Philosophy - PhD from Vrije Universiteit Amsterdam (VU Amsterdam). In addition, Dr. Simioni has obtained several certifications, including a Six Sigma Yellow Belt from 6sigmastudy in 2021, a Scrum Fundamentals Certified (SFC) from SCRUMstudy in 2020, a Data Science with KNIME Software (L1) from KNIME in 2020, and a Practical Machine Learning from Coursera in 2019.

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