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. Project description This PhD project focuses on advancing the scientific computing foundations of quantum spin dynamics by developing efficient numerical algorithms for modeling complex, open quantum
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information, for example data derived from remote sensing, use point process models from the field of spatial statistics to model clustered patterns across the landscape, and develop methods for estimating
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the leadership of Henrik Björklund, Johanna Björklund, and Loïs Vanhée. This is a critical first step toward mitigating the social harms of large language models and other generative AI systems such as the
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inventorying forest biodiversity. Possible areas include: indicators of functional or taxonomic diversity species-specific or habitat-based monitoring combinations of field data, remote sensing, and modelling
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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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of computation, and thus continuous aspects, into rule-based models of graph transformation in order to combine the individual strengths of both paradigms. Rule-based models are transparent and explainable