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databases, design and analysis of virus-derived RNA libraries, and development of machine learning models for detecting functional elements in viral metagenomic datasets. This project is a
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-throughput experimental approaches such as non-coding CRISPR screens, the Massively Parallel Reporter Assay (MPRA), saturation mutagenesis, and synthetic sequence design, alongside machine-learning
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, multiple machine‑learning algorithms, MOFA and multi‑omics pathway analysis. · Strong background in experimental design, quantitative data analysis, and scientific writing, evidenced by first-author
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, machine learning, research methodology, responsible conduct of research, scientific communication, and the translation of computational models into clinically meaningful tools. Individual development
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study cancer , with the goal of discovering the most effective strategies for prevention and treatment. We apply integrated machine-learning methods combined with genome engineering technology to
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related fields. Prior lab experience is preferred but not required. Candidates with research experience in machine learning and artificial neural networks, are strongly encouraged to apply. The candidate
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plus Experience applying machine learning, artificial intelligence, and large language models to research a plus The anticipated start date is September 1, 2026. The postdoctoral
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, and Economics. We are particularly interested in candidates with a research focus on algorithms, blockchains and cryptocurrency, causal inference, game theory, learning, machine learning, market
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particularly welcome research that advances computational methods, including the use of artificial intelligence and machine learning for discovery in applied mathematics and scientific computing. Research
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that advances computational methods, including the use of artificial intelligence and machine learning for discovery in applied mathematics and scientific computing. Research motivated by