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will develop statistical and machine-learning approaches for integrating AIRR-seq with single-cell RNA-seq, paired BCR/TCR data, genomics, immune phenotyping, germline variation, and clinical
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, depending on experience. The successful candidate will develop statistical and machine-learning approaches for integrating AIRR-seq with single-cell RNA-seq, paired BCR/TCR data, genomics, immune phenotyping
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. Education and scholarly development The postdoctoral associate will receive structured education in computer vision applications in medical imaging, machine learning, research methodology, responsible conduct
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. Position Overview The successful candidate will develop and apply advanced computational and machine learning methods to large-scale genomic, clinical, and imaging datasets, working across one or more of the
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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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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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-scale human datasets. You will: - Build and apply machine learning and deep learning models to multi-scale (cells, brains, patients), multi-modal (omics, biosensor data, vision, electronic health data
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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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, 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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skills, including generalized linear models, multiple machine‑learning algorithms, MOFA and multi‑omics pathway analysis. · Strong background in experimental design, quantitative data analysis, and