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learning, deep learning, and large language models (LLMs), for the analysis of high-throughput multi-omics datasets (especially single-cell and spatial omics) and large textual corpora (e.g., scientific
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machine learning. The research at DTU Bioinformatics is focused on bioinformatics and computational analyses of large amounts of data generated within biological, biomedical and biotechnological and life
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data and clinical information. Applicants must hold (or be close to completing) a PhD in a relevant field and have expertise in modern computer vision and AI research. Experience with biomedical data
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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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Pneumatic Tires, Structure-Process-Properties Relationships. How will you contribute? Do you have proven skills in data analysis, machine learning, as well as in mathematical and computational modelling? You
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of machine learning and health sciences, with unique access to experimental and clinical data. Embedded in Munich’s thriving AI landscape, fellows benefit from world-class facilities, interdisciplinary
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fields: AI (machine learning, big database, etc) Semiconductors
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found on hpc.uni.lu . The activities include classical HPC applications such as simulation and modeling, but also artificial intelligence and machine learning, bridging computational science, with data
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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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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