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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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to the development of the research milieu. Requirements PhD degree in a field closely related to the position (e.g., computerized image analysis/processing, machine learning, artificial intelligence, data science
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, and doctoral students active on both campuses. Learn more about the Department of Archaeology, Ancient History, and Conservation here: Department of Archaeology, Ancient History and Conservation
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experience with machine learning techniques in general and neural networks in particular will be highly beneficial. About the employment The employment is a temporary position of 2 years according to central
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using genetic data from family-based studies as well as -omics data for integrative deconvolution and machine learning methods for prognosis and therapeutic biomarker development. The collaborative
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and refined our pioneering AI-driven methods. This project focuses on improving protein structure prediction, design, quality assessment, and dynamics using innovative machine learning techniques. You
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deconvolution and machine learning methods for prognosis and therapeutic biomarker development. The collaborative research may include but is not limited to software tool dissemination, biology discovery, and
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(e.g., power electronics or machine learning applications in power systems). The PhD degree must have been awarded no more than three years prior to the application deadline*. The ideal candidate has
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science. You will be part of a dynamic research group with expertise in Earth Observation, geoinformatics, and machine learning, offering an excellent environment for advancing your research and building