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, machine-learning model development, structural sensing and health monitoring, conducting physical experiments, and validation of computational models. Required Qualifications: A successful applicant must
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electrophysiology data obtained through collaborations and perform cross-species comparisons. We use machine learning techniques for neural data analysis and computational modelling with a special interest in
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will focus on developing efficient foundation models to medical image analysis. Foundation models offer a scalable and adaptable solution for medical image analysis by learning generalizable
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Academic Job Category Faculty Non Bargaining Job Title Postdoctoral Research Fellow in Machine Learning for Computational Pathology, Medical Imaging, and Clinical Text Analysis Department Bashashati
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implement machine learning models dedicated to the prediction, interpretation, and quantitative analysis of Raman vibrational spectra, establishing explicit links between structure, local chemical environment
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: Further Info: https://argonne.wd1.myworkdayjobs.com/Argonne_Careers/job/Lemont-IL-USA/Postdoctoral-Research-Associate---Machine-Learning-in-High-Energy-Physics-Detector-Operations_421270 9700 South Cass
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in data integration, model design, and large-scale training by combining multi-modal scientific data, knowledge graphs, physics-aware machine learning, and GPU/HPC computing to develop transparent and
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. The postdoctoral fellow will lead efforts to develop novel machine learning models for integrating omics datasets (e.g., genomic, transcriptomic, epigenomic, proteomic, metabolomic) with relevant molecular pathways
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requirements and focusing on data-value maximisation. This project will utilise innovative machine learning methods and tools from process systems engineering to simultaneously optimise product quality and the
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single‑cell omics, AI machine learning, and translational biology. The role involves collaboration with academic research group(s), with a strong focus on bridging advanced computational methods