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avenues of research rather than a required work plan. They may be pursued individually or in combination, and we welcome other creative and strategic approaches. Statistical or machine-learning approaches
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the Cornell Lab to build the next generation of biodiversity monitoring systems. By combining the complementary strengths of community science observations and acoustic data, we aim to develop scalable
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research project in any of several areas relevant to this topic, and to explore multiple lines of research in parallel. Most projects employ a combination of bacterial genetics, high throughput genetic
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to this topic, and to explore multiple lines of research in parallel. Most projects employ a combination of bacterial genetics, high throughput genetic screens, biochemistry, ribosome profiling (ribo-seq
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. This project will examine the varied roles of distinct D. melanogaster AMPs in fighting a spectrum of bacterial infections, using evolutionary and functional genetic approaches in combination with cell biology
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. The lab focuses on understanding how early-life nutrition shapes the gut microbiome and its metabolic output, using a combination of microbiome sequencing approaches, mass spectrometry-based metabolomics
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for robust and scalable quantum information processing. These projects will involve a combination of analytical and numerical approaches and will connect closely with ongoing experimental efforts. A Ph.D. in
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Genome Curation Assistant (Jarvis ): an AI system that combines modern machine learning approaches with large-scale biological data to automate genome curation by detecting, interpreting, and correcting
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metabolic output, using a combination of microbiome sequencing approaches, mass spectrometry-based metabolomics, and research models of host-microbe interactions (including gnotobiotic systems). The lab's
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of architectures for robust and scalable quantum information processing. These projects will involve a combination of analytical and numerical approaches and will connect closely with ongoing