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include: Biomedical sensing and physiological monitoring Edge intelligence and energy-efficient machine learning hardware Radar and wireless signal processing and communications The successful candidate
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and deep learning methods for large-scale genomic, clinical, and imaging biobank data, with stable multi-year NIH support. The Zhi Laboratory has a sustained track record of methods development
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approaches such as non-coding CRISPR screens, the Massively Parallel Reporter Assay (MPRA), saturation mutagenesis, and synthetic sequence design, alongside machine-learning models of regulatory grammar
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for three references upon request. ABOUT THE YALE CENTER FOR EMOTIONAL INTELLIGENCE Emotions drive learning, decision making, creativity, relationships, and health. Our goal is to design effective approaches
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studies how the infant brain learns about the social world and how early-life experience shapes neural circuits with long-term consequences for learning, attachment, and mental health. Our work focuses on
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, 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 collaboration with the
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external collaborators of the Bakshi lab. This position will thus prepare the candidate for an independent research career. Training: The post-doctoral associate will learn skills in research study design
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Computational Translation at Yale (IMPACT-Y) study. This large-scale, longitudinal project is designed to advance computational psychiatry by integrating mechanistically informed behavioral tasks with theory
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, comparative genomics, machine learning, and evolutionary analysis to address fundamental questions in molecular biology and human disease. Responsibilities Develop computational pipelines for the discovery
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in statistics, machine learning, clinical investigation, and research leadership; and develop an individual development plan addressing publications, funding, communication, networking, and the next