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Field
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areas include: indicators of functional or taxonomic diversity species-specific or habitat-based monitoring combinations of field data, remote sensing, and modelling new techniques for detecting and
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both experimental development and theoretical modelling. Therefore, it requires the candidate to have a solid background in physics, electronics, and mathematics, along with strong practical experimental
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electrification of the broader economy affect the competitiveness of power producers via a Nash-Cournot model of regional energy sectors. The ensuing bottom-up equilibrium model with both flexible demand and
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science, connected to MDU’s existing research areas. The PhD project focuses on the development of a large language model (LLM)-based AI agent specialized in energy decision-making, including: development of AI models
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– Adsorption and reaction of small gaseous non-metal oxides on model mineral particles under light illumination. The position is for four years of doctoral studies, including participation in research and
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, bioinformatics, and ecological modeling. Results will inform future biodiversity monitoring frameworks, adaptive forest management, and conservation policy. At the Department of Wildlife, Fish and Environmental
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in this project. As successful candidate, you will investigate predisposition of trees to drought stress by long-term fertilization, both experimentally and with modelling. You will analyze long-term
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of different remote sensing and mapped data, statistical modelling, mapping of fire risk and analyzing the effect of forest management for fire behavior. Considered remote sensing data includes airborne and
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help reconstruct biodiversity time series and assess how forest management impacts species dynamics. The position will involve lab and field work, bioinformatics, and ecological modeling. Results will
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-year project the student will primarily do research in silviculture and modelling of growth and yield in boreal forest. The PhD student will get a research education in silviculture with training in