Eneryield
Karl Bäckström, Ph.D., has a diverse work experience that spans several companies and roles. Karl began their career in 2015 as a Programmerare at Ericsson, where they worked until December 2016. In 2016, they joined CPAC Systems AB as a Programmer and contributed until April 2017. From June to December 2017, they served as a Project Research Assistant at Chalmers University of Technology.
In 2018, Karl Bäckström started their Ph.D. journey at Chalmers University of Technology, focusing on their studies as a Ph.D. Student. Karl continued this role until January 2023. During their time as a Ph.D. student, they also co-founded Eneryield in 2019, where they held the position of Co-Founder until January 2023. Following this, they assumed the role of Technical Lead at Eneryield. No specific end date is provided for this position.
Overall, Karl Bäckström's work experience demonstrates their expertise in programming, research, and leadership roles within the tech industry.
Karl Bäckström, Ph.D. completed their education in a chronological order. Karl obtained their Studentexamen in Mathematics from Hvitfeldtska gymnasiet during the period of 2010 to 2012. Following that, they pursued a Bachelor's degree in Engineering Mathematics at Chalmers University of Technology from 2012 to 2015. Karl furthered their education by earning a Master's degree in Engineering Physics from the same institution between 2015 and 2017. Karl continued their studies at Chalmers University of Technology and obtained a Licentiate of Technology in Computer Science from 2018 to 2021. Finally, they completed their educational journey by achieving a Doctor of Philosophy (Ph.D.) in Computer Science from Chalmers University of Technology, which they pursued from 2018 to 2023.
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Eneryield
Eneryield is developing state-of-the-art machine learning methods for intelligent analytics of power quality, based on several years of research at Chalmers University of Technology. The methods are capable of classifying and finding the root-cause of power quality events, which gives the necessary information to take action and mitigate disturbances, leading to reduced energy losses, reduced wear and tear of components and less stops in production. They can also model the power grid, which makes it possible to predict power quality events and variations. This is used as a control component to make power electronics react in real-time with increased efficiency.