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for molecular dynamics (MD), slashing computational costs by orders of magnitude and enabling breakthroughs in drug design and materials science. The position bridges machine learning and molecular science, with
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/department-of-computing-science/ Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We focus on data-driven models for complex and temporal data
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description and working tasks The project will develop privacy-aware machine learning (ML) models. We focus on data-driven models for complex and temporal data, including those built from synthetic sources
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postdoctoral researcher with a focus on AI trustworthiness modeling on multimodal data and machine learning models. The Department of Computing Science has been growing rapidly in recent years, with a focus on
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trustworthiness modeling on multimodal data and machine learning models. The Department of Computing Science has been growing rapidly in recent years, with a focus on creating an inclusive and bottom-up driven
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. Research topics include: Development and validation of DORIS data processing and modeling Implementation of improved models for DORIS satellites and ground systems Cross-analysis of DORIS and other geodetic
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, obtained within the last three years prior to the application deadline Strong background in machine learning, statistical modeling, and big-data analytics. Experience with infrastructure or transportation
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consists of 18 research groups covering a wide range of mathematical disciplines – from pure and applied mathematics to numerical analysis and optimization, as well as mathematical statistics and machine
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machine learning models in simple, standalone devices that are capable of advanced processing. Building on our work on solution-based neuromorphic classifiers (https://doi.org/10.1002/advs.202207023
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of large-scale machine learning models (e.g., LLMs) in a meaningful way, we, therefore, need new scalable methodologies that can efficiently and accurately capture, represent, and reason about uncertainties