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dynamically with external knowledge sources, retain memory across sessions, and autonomously generate responses and actions. While their adoption brings transformative benefits, it also exposes them to new and
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series data. Large data sets come with significant computational challenges. Tremendous algorithmic progress has been made in machine learning and related areas, but application to dynamic systems is
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, Accelerator Mass Spectrometry (AMS) ) for ultra-trace detection of actinides. Integrate experimental results into dynamic geochemical and transport models to predict future radionuclide behavior under different
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. CNC and robotic machining, additive manufacturing, and digital simulation, the research investigates how future aesthetics and methods of production might be shaped in this dynamic interplay between
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the field. The project is a collaboration between Linköping University and Lund University and will be conducted within a dynamic and collaborative research environment. As a PhD student, you devote most
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generative models, geometric machine learning, dynamical systems, and/or multi-modal learning. From the materials science perspective, our primary focus will be on ultra-thin, so-called, 2-dimensional