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contribute to analyses and processing of data. They will become familiar with high performance computing environments and parallel processing, as well as cloud computing infrastructure. They will
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predict molecular subtypes and therapeutic responses. These tools power a precision oncology framework that enables data-driven treatment selection. In parallel, we generate patient-derived 3D
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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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organisms. Please note that we have a parallel search for computational neuroscientist faculty members at https://apply.interfolio.com/190740 . The successful candidates will be embedded in a richly
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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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over time, and heterogeneity of care partner factors are also of interest. The position is supported by the National Institute on Aging (NIA) IM bedded P ragmatic A lzheimer’s disease (AD) and AD
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as scRNA-seq, scATAC-seq, NGS, WGS/WES, GWAS, etc. Apply statistical genetics analysis frameworks such as LDSC, p-HESS, eQTL, QTL-GWAS, Mendelian randomization, statistical colocalization, etc
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systems . The position focuses on developing integrated circuits and hardware systems that merge sensing, signal processing, and inference directly at the analog and RF interface . This approach enables
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and unstructured data in the electronic health record (EHR) as well as MyChart data, with the opportunity to work on applications of machine learning/deep learning/ Natural Language Processing in
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training. The trainee will gain experience in both translational research and clinical trials. The trainee will learn phlebotomy, processing of blood specimens for protein and genomic analysis as well