Itilekha Podder

Postdoctoral Researcher Itilekha Podder received her joint Master’s degree from Delft University of Technology and Budapest University of Technology and Economics in 2020 through the European Institute of Innovation and Technology program. During her studies, she specialized in network media services and applications for autonomous vehicles, with a minor in innovation and entrepreneurship.

In 2020, she was awarded merit certificate for her keen sense of responsibility and outstanding dedication to her studies by the Faculty of Electrical Engineering and Informatics at Budapest University of Technology and Economics. For her Master’s thesis, she worked on accuracy and latency measurement of different connectivity types for real-time object detection using convolutional neural networks in collaboration with Ericsson. Her minor thesis focused on exploring opportunities for utilizing connectivity technologies for drone applications in collaboration with Telefónica.

She completed her PhD at the Eötvös Loránd University (Doctoral School of Informatics) as an European Institute of Innovation and Technology Doctoral Fellow, in close collaboration with Robert Bosch GmbH. Her doctoral research focused on the development of methods for deploying deep learning algorithms in autonomous systems and Industry 4.0 environments. In particular, her work addressed AI-driven optimization and analysis of micro-electromechanical systems (MEMS) manufacturing processes, combining machine learning with industrial production data. Her research was conducted under the supervision of Dr. habil Udo Bub and DR. Tamas Fischl.

She successfully completed her PhD on January, 2026, with the distinction summa cum laude.

In March 2026, she joined the Spinal Cord Artificial Intelligence Lab as a Postdoctoral Researcher at ETH Zurich in collaboration with Swiss Paraplegic Research, located in Nottwil. Her research focuses on machine learning methods for processing motion capture data and developing advanced computational pipelines for the design and evaluation of assistive walking technologies. In this role, she will lead research on data acquisition strategies, machine learning model development, and clinical validation of AI-driven systems in collaboration with clinical and engineering partners. She is responsible for data management, algorithm development, and machine learning training, and supervises doctoral and master’s students involved in the project.