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University. This research opportunity will be focused primarily on the development and application of novel computational algorithms to analyze and integrate diverse omics datasets, including bulk and single
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/or experience with large-scale data analysis, algorithm development, or computational modeling. Required Qualifications: Doctoral degree in linguistics, cognitive science, psychology, hearing and
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of powerful computing tools, and innovation in quantitative and qualitative research methods are opening a new frontier for social scientists to explore bold, inventive research questions. In this era
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of project researchers, investigators, and/or managers. This position will support the Immune Behavioral Health/ Pediatric Acute-onset Neuropsychiatric Syndrome (PANS) Research program. The ideal candidate
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research. The ideal fellow will be interested in developing and applying novel computational algorithms to novel datasets generated in the setting of non-neoplastic and neoplastic disease. Key
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will be focused primarily on the development and application of novel computational algorithms to analyze and integrate diverse omics datasets, including single-cell RNA-seq, spatial transcriptomics and
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into the brain's algorithms of perception and cognition while serving as a key resource for aligning artificial intelligence models with human-like neural representations. As part of this project, we are seeking
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models of tuberculosis (TB). The lab couples Mycobacterium marinum, a close genetic relative of M. tuberculosis which causes TB-like disease in fish, with transparent and genetically tractable fish species
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large datasets. Create databases and reports, develop algorithms and statistical models, and perform statistical analyses appropriate to data and reporting requirements. Use system reports and analyses
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disease progression. This includes integrating LLMs with structured data sources to develop robust computational phenotyping algorithms and scalable models for real-world evidence generation. The role will