16 machine-learning "https:" "https:" "https:" "https:" Postdoctoral positions at Yale University in United States
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Show all breadcrumbs Postdoctoral Associate Position on Blockchain and Machine Learning in the Dr. Kuo Lab at Biomedical Informatics and Data Science, Yale University Tuesday, April 8, 2025
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, machine learning and deep learning methods, and the responsible conduct of research. Training: The candidate will receive training in implementing advanced machine learning and deep learning models, and NLP
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non-coding CRISPR screens, the Massively Parallel Reporter Assay (MPRA), saturation mutagenesis, and synthetic sequence design, alongside machine-learning models of regulatory grammar. Responsibilities
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families of algorithms for cryo-electron microscopy (cryo-EM) and broader applications in data science * Protein structure prediction: Combining traditional and machine learning approaches to protein
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informatics, or related field. • Strong knowledge and experience in natural language processing, machine learning, and deep learning. • Strong written and oral communication skills in English. • Ability to
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should have a Ph.D. in a relevant quantitative field (e.g. computer science, Bioinformatics, Statistics, Computational Biology, Statistical Genetics), a strong background in machine learning or
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will use causal machine learning and other statistical methods to understand how treatment effects vary across individuals, populations and over time. Methodological work will address real-world
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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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, 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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-throughput experimental approaches such as non-coding CRISPR screens, the Massively Parallel Reporter Assay (MPRA), saturation mutagenesis, and synthetic sequence design, alongside machine-learning