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the complexities of the human regulome through advanced cell-free DNA profiling and developing cutting-edge computational algorithms and molecular profiling techniques. Our research focuses on early cancer detection
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not required as part of this position. Required Qualifications: Strong mathematical background, including expertise in one or more of the following areas: machine learning, statistics, and algorithms
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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 single-cell RNA
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omics to advance biological and clinical discoveries and develop next-generation theragnostics. The postdoctoral fellows will mainly focus on (1) creating novel computational algorithms to analyze and
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systems. Includes establishing medical reasoning benchmarks and automated / scalable evaluation methods. Developing recommender algorithms to predict specialty care with large-language model based user
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external) provides algorithmic and data-driven solutions providing fairer access to public educational resources. The lab enables school districts to optimize large-scale equitable resource allocation
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single-cell sequencing, spatial transcriptomics, and machine learning algorithms to to understand, at the tissue and organ level, how specific cellular communications—from synaptic connectivity to neural
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algorithms and scalable models for real-world evidence generation. The role will involve both method development and applied research, with opportunities to publish in leading journals, present at top
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cancer epidemiology with a Ph.D. degree in biostatistics, operation research, epidemiology (with strong computation skills), or related fields and hands-on experience in algorithmic implementation and