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innovative methods for processing and analyzing 7Tesla MRI images of different modalities and formats (NIFTI, DICOM, etc.) using machine learning and artificial intelligence techniques. These methods will be
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Department of Biomedical Informatics is a highly dynamic and multidisciplinary environment that has strengths in bioinformatics, global health informatics, imaging informatics, machine learning, mHealth
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field. Proven experience in multi-omics data integration, omics data analysis (genomics, transcriptomics, proteomics, metabolomics, microbiome). Strong expertise in machine learning, deep learning, and
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background in machine learning, predictive modeling, or applied AI Proficiency in Python and/or R; experience with libraries like scikit-learn, XGBoost, TensorFlow. -Experience working with real-world datasets
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rigorous, collaborative research aligned with project goals. Develop and apply deep learning models, particularly in computer vision, NLP, and multimodal systems. Publish in peer-reviewed journals and
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Full time: 25 Hours per week Fixed term: 12 months We are looking for a candidate to join the University of Edinburgh to conduct research on Machine Learning, Reinforcement Learning, or LLM
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computational tools for analyzing immunogenomics data in the context of gastrointestinal autoimmune diseases and Type 1 Diabetes. Lead the application of AI and machine learning to identify novel therapeutic
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terms of research and education, covering all aspects of computer science, including but not limited to algorithms, databases, cloud computing, machine learning, operating systems and security. Jobs
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chemical discoveries for renewable energy, biomedicine, and other areas of societal importance. Coding and/or machine learning experiences are highly valued. Specific projects may involve developing
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on a new project called TRUSTLINE, which is part of the Learning Introspective Control (LINC) DARPA Program. The project aims to develop machine learning (ML)--based introspection and monitoring