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are assessed by the PIs of each project, respectively. For detailed information about every project, click the link. 1. Evolution of Scots pine forests since the last glacial maximum https://www.umu.se/en/ucmr
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and Data Science for Spatial Genomics in Diabetes This position centers on the development and application of machine learning, image analysis, and integrative omics approaches to spatial
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data, to better understand how biological, psychological, and environmental factors contribute to severe mental disorders, their treatment, and outcomes. The Centre has access to a large database of
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us We are TUM’s unique Pathology AI lab developing new machine learning (ML) methods for automatically analyzing digital pathology data and related medical data. Such methods include the automatic
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-based sensor data to enhance the prediction of peatland soil properties and functions. You will focus on leveraging machine learning/deep learning techniques along with explainable artificial intelligence
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-based sensor data to enhance the prediction of peatland soil properties and functions. You will focus on leveraging machine learning/deep learning techniques along with explainable artificial intelligence
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develop a simplified model focusing on the leader stage. You will: Analyze experimental data and microscopic simulations Identify relevant physical features and parameters Apply machine learning techniques
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management machine learning, distributed computing, and resource optimization leveraging the unique computational resources available at ORNL, including the Frontier supercomputer—the world's first exascale
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machines that both learn from humans and help humans learn. The postdoctoral fellow will lead a project using AI technologies to support active learning in young children, by empowering them to create
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record (EHR) as well as MyChart data, with the opportunity to work on applications of machine learning/deep learning/ Natural Language Processing in novel areas of healthcare. The position is open for a