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-year postdoctoral position aimed at the development of a novel organic-electronics technology, termed a light-emitting electrochemical cell (LEC). The LEC is currently attracting increasing interest from
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to humans and are accessible to algorithmic techniques while neural models are adaptive and learnable. The aim of this project is to develop models which combine these advantages. The project includes both
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electronic characteristics. The project’s goal is to develop fundamental understanding and innovative fabrication processes to solve urgent problems in organic electronic devices, and to enable new components
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with big datasets: towards methods yielding valid statistical conclusions” led by Professor Xavier de Luna and Tetiana Gorbach (Statistics). The overall purpose of the project is to develop novel methods
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equity perspectives that emphasize the interrelations between multiple inequities to examine inequities in the health system. It takes its point of departure in an intersectional conceptual framework and
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analysis of complex, longitudinal, and high-dimensional data (e.g., immunometabolic profiles, clinical data, biomarkers). Development and application of predictive models and algorithms for diagnostics
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investigate how public agencies adopt and integrate AI technologies, what forms of automation and augmentation emerge, and how these developments influence organizational structures, professional roles, and
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questions include automated modeling and model simplification/refinement supported by generative AI, system identification, and 3D reconstruction algorithms. Additionally, the research involves developing
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into how algorithmic systems influence the circulation of information and disinformation across digital platforms, and how such processes affect perceptions of credibility, truth, and democratic
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to develop a high-throughput experimental framework to investigate the genetic and epigenetic effects of nanoplastic particles (NPs) using bacterial mutagenesis systems as an initial model. By adapting