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Field
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bioinformatics, with a particular emphasis on performing analysis of high-dimensional data, which can be sequencing and/or imaging-based. Experience working with AI and machine learning approaches are considered a
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, methods and applications of machine learning and deep learning. Experience with frameworks and libraries for deep learning (e.g., PyTorch). Experience with models and libraries in computer vision, for image
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extraction) that can be miniaturized and integrated into portable devices. Perform SERS measurements and data analysis of SERS data (e.g., using machine learning). Develop, test and apply new fiber-based SERS
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on the development of Bayesian statistical/machine learning methods for the data integration analysis of high-throughput imaging and molecular data (i.e., genome, transcriptome, epigenome, and more). The methods would
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unified framework that can understand and generate human-like text while simultaneously processing and analyzing medical images. The model will be trained on a diverse dataset of medical images and