Background
I’m a 26-year-old from Oslo, Norway, with a master’s degree in Applied Physics and Mathematics from NTNU (Norwegian University of Science and Technology, Trondheim), specializing in Industrial Mathematics. The Erasmus Scholarship allowed me to spend my fourth year at Universitat Polytècnica de Catalunya, Barcelona. My research interests include numerical mathematics, differential equations, and neural networks, with my thesis focusing on combining reduced order modelling with artificial neural networks. I enjoyed working as a teaching assistant in subjects like Introduction to Scientific Computing and Differential Equations and Fourier Analysis. I have industry and consulting experience, including anomaly detection for Telenor and predictive maintenance of railway tracks for Bane NOR. I also worked as a consultant for PwC Oslo, performing data analysis for retail customers. Outside of work, I enjoy running, hiking, playing football, and exploring new foods and wines.
Motivation
I am excited to pursue the DC10 – NEURAL project: Machine learning for deterioration prediction based on digital information streams. This project aims to develop prognostic deterioration models using sensor data and machine learning. The challenge of working with limited data in this field intrigues me, and I am keen to explore how to optimize neural network models for practical applications. Developing a machine learning tool for Pedelta that can be used in industry is an ideal goal, and I look forward to the entire process, from investigating sensor locations to analyzing data results. Contributing to the bridge industry with data science solutions will be rewarding, and I hope this project will lead to service life augmentation for bridges, combining technological advancement with environmental sustainability.