André Pereira completed his Master's degree in Mechanical Engineering at the University of Coimbra (UC) in 2014. In the same year, he started his research activity under a FCT project, associated with the numerical modelling of nanotubes, opening a new research area in the Centre for Mechanical Engineering, Materials and Processes (CEMMPRE). In 2019, he completed his Ph.D. in Mechanical Engineering (highest classification) under a FCT grant.

In this work, strategies were developed for the efficient and accurate identification of elastoplastic constitutive parameters of metal sheets. During his PhD, he was appointed as the representative of the Mechanical Engineering PhD students. After his PhD, and based on the evaluation of his scientific results, he became an integrated member of the scientific committee of CEMMPRE and won an international research contest within the "EZ-SHEET" project, where he developed new identification strategies using robust optimisation algorithms.

Since 2023, he is a post-doc researher after winning one of the most competitive FCT calls (Individual Call to Scientific Employment Stimulus). In his post-doctoral work, he has developed a new research area at CEMMPRE related to the application of machine learning techniques in sheet metal forming processes for the robust analysis and uncertainty modelling. He is currently the principal investigator of the “RealForm” project related to this topic.

From the above research activities, André Pereira published 24 papers in international journals, 26 works in international conferences, 2 book chapters and 1 book (editor). Participated as a research member in 15 project applications, 7 of which were approved with an accepted amount of €2,966,465.

Complementary to his research activities, since 2019 he has been an Invited Assistant Professor at UC, in the disciplines of 'Numerical and Computational Methods', 'Elasticity and Plasticity' and 'Solid Mechanics'. He has supervised several MSc and PhD students, as well as young and post-doc researchers in research areas, namely computational solid mechanics, machine learning techniques, robust and sensitivity analysis applied to sheet metal forming processes.

Numerical Simulation of Forming Processes and Nanotubes, Uncertainty Analysis, Parameter Identification and Machine Learning