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Luis Fernando Salazar Betancourt, Ph.D.

Engineering Manager, CAE at Tech Soft 3D

Luis Fernando Salazar Betancourt, Ph.D. has extensive experience in the field of CAE software engineering and research. Luis Fernando has worked as a Senior Tech Lead at Tech Soft 3D, where they served as the primary liaison for the team and demonstrated high-level expertise in CAE products. Prior to this, they worked as a Senior CAE Software Engineer at the same company, focusing on the development and maintenance of high-performance libraries for finite element numerical simulation. Additionally, Dr. Salazar Betancourt worked as a CAE Software Engineer at Tech Soft 3D, where they developed innovative solutions for 3D analysis and visualization for CFD and FEA.

Before joining Tech Soft 3D, Dr. Salazar Betancourt worked as a Research CAE Software Engineer at TRANSVALOR S.A., where they worked on a software solution for numerical simulation of metal phase transformation processes. Luis Fernando also gained experience as an Innovation Project Manager and Research CAE Software Engineer at Plastic Omnium, where they completed their Ph.D. thesis in collaboration with Mines-ParisTech.

Dr. Salazar Betancourt holds a Ph.D. in Computational Mechanics and Materials from MINES ParisTech. Their research during their Ph.D. focused on numerical modeling of high fiber reinforced composites for simulation during the filling and phase transformation. Luis Fernando gained further experience as a CAE Software Engineer intern at TRANSVALOR S.A., where they worked on software development for thermal interaction in continuous casting processes.

Prior to pursuing their Ph.D., Dr. Salazar Betancourt completed a Mastère Spécialisé MAPMOD - Materials, Processing, and Modeling at MINES ParisTech. During this program, they studied computational mechanics and gained knowledge in numerical analysis, continuum mechanics, heat transfer, structural mechanics, and finite elements.

Dr. Salazar Betancourt also gained experience as a CAE Software Engineer intern at Siemens, where they conducted validation of a computational model for estimating pressure loss and air split in gas turbine combustion chambers. Additionally, they worked as a Junior Mechanical Engineer at Interamericana de Cables Venezuela, where they successfully addressed ergonomic issues in lifting heavy cables from a machine.

Overall, Dr. Salazar Betancourt has a strong background in CAE software engineering and has contributed to the development of innovative solutions in the field. Luis Fernando has demonstrated expertise in numerical simulation, software development, and the understanding of physical processes in various industries, including automotive and metal transformation.

Luis Fernando Salazar Betancourt, Ph.D. has a diverse and comprehensive education history. Luis Fernando earned their Ph.D. in Computational mechanics and materials from Mines Paris, where they specialized in Computational and Applied Mathematics from 2013 to 2016. Prior to that, they completed a Post Master program in materials, processing, and modelling, also at Mines Paris, in the field of Mechanical Engineering, from 2012 to 2013.

In 2010 to 2011, Luis participated in an Exchange Program for two semesters at the University of Stuttgart, studying Mechanical Engineering. Their foundational education in Mechanical Engineering was obtained at Universidad Simón Bolívar, where they completed their BAC+5 degree (equivalent to a European master) from 2006 to 2011.

Furthermore, Luis has also obtained certifications to enhance their knowledge and skills in the field of Machine Learning. These certifications include "Convolutional Neural Networks," "Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization," "Structuring Machine Learning Projects," and "Neural Networks and Deep Learning." All these certifications were obtained from Coursera in 2020.

Overall, Luis Fernando Salazar Betancourt, Ph.D. possesses a strong educational background in Mechanical Engineering and Computational Mathematics, along with additional expertise in Machine Learning acquired through certifications.

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