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learning methods. Develop deep learning architectures (e.g., variational autoencoders, graph neural networks, transformers) for cross-omics data representation and feature extraction. Apply multi-view
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communication) Willingness to learn and confront new challenges Preferred Qualifications Doctoral research conducted in the area of machine learning for healthcare and related topics Deep knowledge of multi-modal
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Description Post-Doctoral Fellow Position in Medical Image Processing (Deep Learning for Trauma CT) The Trauma Radiology AI Lab (TRAIL) in the Department of Radiology & Nuclear Medicine at the University
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simulations of PDEs, deep learning, neural networks. Our research interest: Our focus is on theoretical and computational biological physics, ranging from the study of molecules to cells. We strive to leverage
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ethical frameworks. Proficiency in Python and experience with relevant libraries for AI/ML development. Experience with advanced AI methodologies including deep learning, transfer learning, and neural
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, research statement, and a list of up to three professional references (include name and email address for each reference). Posting Date 07/15/2025 Closing Date Open Until Filled Yes First Consideration Date
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CBS - Postdoctoral Position, Artificial Intelligence Applied to Metabolomics for Health Applications
metabolomics data from clinical studies. Apply deep learning models (e.g., autoencoders, variational autoencoders, graph neural networks) for biomarker discovery, disease classification, and patient
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” skills coupled with in silico data analysis and QC skills. Deep understanding of molecular protocols and capacity to “tear down” protocols, identify opportunities for improvement, and development
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genomics, virtual cell models Graph-based neural networks, optimal transport Biomedical imaging, deep learning, virtual reality, AI-driven image analysis Agentic systems, large language models Generative AI
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successful candidate will possess skills in natural language processing and deep learning. Experience of studying the robustness and generalisability of LLM would be beneficial. This is a full time post (35