Kamilla Hauknes Sjursen
Field of work
I have a background in energy and environmental engineering and PhD in Computer Science from HVL and work as an Assistant Professor at the Deparment of Civil Engineering and Environmental Science.
I have a great interest in science and technology, especially related to water! I teach physics and technology in the teachers education, hydrology and hydropower in the Bachelor's programme in Energy Transition and Master's programme in Climate Change Management, and in snow and avalanches in the Bachelor's programme in Geology and Geohazards.
My research is in the intersection of computer science and glaciology, where I develop mathematical models to investigate past and future glacier changes and their relation to climate. I build and program mass balance models that use climatic and topographical data to simulate changes in glacier mass and runoff. In my PhD project I focused ton glaciers in Norway, and in particular in Jostedalsbreen, the largest ice cap in mainland Europe. The results of the modelling are a contribution to the JOSTICE project, which aims to investigate the physical and societal impacts of climate change effects on Jostedalsbreen.
A focus of my research is to investigate how statistical methods (e.g. Bayesian methods and machine learning) can be used to improve predictions in glacier mass balance models. Robust models with accurate assessments of uncertainty are an important basis for evaluating the impacts of glacier melt on local tourism, hydropower production, agriculture and landscape planning.
Courses taught
- Physics in MGBNA/MGUNA201
- Physics in MGBNA/MGUNA301
- Hydrological modellering and measurements in GE-487/PL4-303 (Environmental Hydrology and) Runoff Management
- Applied Exercises in Energy Transition (FE402)
- Snow science and avalanches (GE488)
Research areas
- Modelling mass balance and runoff from glaciers
- Statistical methods and optimization in glacier modelling
- Climate change effects on glaciers
Research groups
Publications
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A minimal machine learning glacier mass balance model
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Minimal Machine Learning Glacier Mass Balance Models
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Spatiotemporal variability in mass balance of Jostedalsbreen ice cap 1960-2020, using a temperature-index model with data assimilation
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Modelling mass balance of glaciers in Norway
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Reconstructing mass balance of glaciers in Norway using machine learning