I'm Anas Jnini, specialized in Deep Learning and High Performance Computing (HPC), with an emphasis on Physics-Informed Neural Networks. Having completed my Bachelor's and Master's degrees in Mechanical Engineering at the Swiss Federal Institute of Technology in Lausanne (EPFL), my keen interest in the intersection of physics and AI propelled me towards a Ph.D. at the Università degli studi di Trento. Here, my research is centered around leveraging accelerated computing technologies for building efficient Model-Based Digital Twins and AI frameworks.

My experience spans various roles, including my stint at Aeroconseil SA, where I was involved with Aerospace Engineering, working on the Airbus XLR project's flight control and autopilot systems. For my Master's thesis at the Biorobotics lab at EPFL, I focused on developing a framework that uses Physics-Informed Neural Networks for learning the dynamics of musculoskeletal systems.

I've honed my skills in several programming languages and technologies like C, C++, Python, and CUDA. In addition, I have substantial experience using PyTorch for building and deploying Deep Learning models. My work primarily lies at the crossroads of Deep Learning, High-Performance Computing, Digital Twins, and Physics-Informed Neural Networks, where I seek to create innovative solutions to complex problems.