Collaborative research project with NTNU and Solution Seeker, focused on developing and evaluating new machine learning methods for industrial data.
During the summer of 2022, I had the opportunity to work on an applied research project with Solution Seeker and NTNU. The goal was to explore and test new machine learning techniques tailored to industrial time-series data.
My work involved reviewing state-of-the-art deep learning architectures, training and evaluating models, and analyzing their performance in real-world industrial scenarios. This experience gave me valuable hands-on exposure to advanced machine learning concepts and the practical considerations of applying them in a research setting.
The project not only helped prepare me for my project and master’s thesis but also gave me a better understanding of how to critically assess and compare ML methods from a scientific perspective. I collaborated closely with both academic and industry supervisors, and read more research papers in one summer than ever before.
You can learn more about Solution Seeker here.