From engineer to industrial doctoral student
Björn Norén is doing his PhD at Mid Sweden University, where he combines his background in computer engineering with research on how AI can streamline the management of log data to improve systems and user experience.
Can you tell us briefly about your background?
I have studied the Master of Science in Computer Science and Engineering here at Mid Sweden University. It was a broad education where we also got to study Artificial Intelligence, and after my studies I have worked at Valmet's R&D where we developed programs that would control machines using AI. Today, I am working at GDM, where I am conducting my research.
How come you did your PhD at Mid Sweden University?
I have studied at Mid Sweden University before and it is a university that I warmly recommend. The teachers are professional and the university is modern and relatively small, which helps them to adapt quickly to modern technology. I'm also very passionate about AI, so getting the opportunity to do a PhD was something I didn't want to miss.
What made you interested in the program?
Doing a PhD means that you get the chance to really immerse yourself in a certain subject, and I think working with AI is great fun. AI is also a fairly new industry, so it is not very easy to find long-term services in Sundsvall at the moment. Therefore, this felt just right as it is a position of at least 2 years where you get the chance to really specialize.
What does it mean to be an industrial doctoral student?
Being an industrial doctoral student means that you carry out your doctoral education in close collaboration with a company or an organization. This means that the research is strongly linked to practical applications and that you often work both at the university and in industry. The goal is for the research to benefit both academic development and the company's needs, creating a direct link between theory and practice.
What are your research studies about?
Companies today store and handle large amounts of data, a large part of which is in the form of logs. These logs can contain useful information about how system failures can be addressed or improve user experiences for users who use services, but the amount of data can be overwhelming for humans, so the hope is that AI will simplify analysis by automatically detecting patterns and anomalies. My work focuses on developing AI models that can efficiently interpret these logs, helping companies to detect issues faster, optimize system performance, and improve the user experience.