João L. A. de Melo was born in Pico island, Azores-Portugal. He received the B.Sc. and M.Sc. degrees in electrical and computer engineering from the Faculty of Sciences and Technology of NOVA University of Lisbon (FCT-UNL), Lisbon, Portugal, in 2008 and 2010, respectively, and the Ph.D. degree in continuous-time sigma-delta modulators (ΔΣMs) using passive RC integrators from FCT-UNL in 2017. From 2002 to 2004, he was a Team Coordinator of sound installations and an Analyst of sound systems with Audium S.A., Portugal.

1. In 2012 I finished my electronic engineering degree, followed by a Microelectronic Engineering Honours Degree in 2013, and then a research master’s degree in 2014 at the University of Pretoria in South Africa.
2. Thereafter, I graciously accepted a Commonwealth Scholarship to study a PhD at The University of Edinburgh in September 2015. Under the supervision of Prof.

Mateus is an experimental physicist with focus on the characterisation of new silicon pixel detectors. He obtained his Ph.D. degree from Geneva University with his thesis work on new monolithic and capacitively coupled hybrid pixel detectors, both based on the HV-CMOS technology. Mateus is now a fellow at CERN working on new pixel detector hybridisation techniques using Anisotropic Conductive Films, and on the characterisation of the new silicon sensors for the CMS High Granularity Calorimeter.

Anna Vaskuri received her doctorate in the field of Measurement Science and Technology from Aalto University, Finland in 2018. After her graduation, Vaskuri worked for 2.5 years at the National Institute of Standards and Technology, CO, USA developing high-accuracy absolute laser radiometers. In October 2021, she joined CERN to develop new aluminium-stabilized high-temperature superconducting (HTS) conductors for future detector magnets with high current carrying capability of several kiloamperes and measurement facilities for characterizing such conductors.

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Dalila works on fast calorimeter simulation using Machine Learning techniques. She has a data science background and during her PhD she designed generative models for fast calorimeter simulation in the ATLAS experiment. Currently, Dalila is working on generic Machine Learning techniques that allow to go beyond specific detector geometries. In this project, she is testing and integrating first prototypes of these techniques into Geant4.

Paul studied physics in Mainz, and quickly identified particle physics as his main field of interest. During his studies, he worked on analysis efforts searching for new physics with the ATLAS experiment, before getting into reconstruction and track reconstruction in particular during his PhD. Here, the focus of his technical work was on efficient geometry modelling and data structures. He is a core developer of the ACTS experiment-independent track reconstruction software package.

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