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. The postdoctoral scholar will contribute to an exciting research program within the Foy Lab (www.foylab.xyz/) https://foylab.xyz/ , developing machine learning, computational, and mathematical models for improving
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research in the natural, mathematical and computer sciences with a focus on the processing, structuring, and analyzing of large amounts of complex data and the development of computational methods and
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depth in some of the following areas (not all are required): Large-scale data analysis and learning analytics methods Experimental or quasi-experimental design; validity and measurement Working with LLMs
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data or large data volumes in all information systems. We contribute methods and algorithms for machine learning, and data mining, including XAI, as well as for data access and query processing. Aarhus
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for high-dimensional dependent data, and data sketching approaches for massive data. Opportunities to Contribute: Develop statistical/machine learning methodology for multi-modal imaging data integration
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intelligence / machine learning, biostatistics, computational biology, or related subject area A track record of previous publications in bioinformatics analysis of large-scale biomedical data, e.g.: omics
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Generation, digital double, human-like avatars. Profile PhD in Computer Vision Background in CS Research experience in gen AI, deep learning Strong computational and analytical skills, as well as experience
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of generative AI tools, use of large language models, machine learning, and ethical frameworks for AI implementation. Ability to apply AI to interdisciplinary research or developing AI models
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. Desirable experience in AI-assisted image analysis, machine learning, computational modelling, or building predictive models from biological imaging or cell–material interaction datasets. Strong experience in
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). This interdisciplinary project investigates how AI — specifically large language model (LLM)-based agents — can act as adaptive social agents to support students' collaborative learning in Challenge-Based Learning (CBL