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Field
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enthusiasm for working at the wet/dry-lab interface alongside experimentalists, clinicians, and trainees Preferred: Experience with cfDNA/cfRNA, single-cell or bulk RNA-seq, or other liquid biopsy / clinical
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background in regulation of gene expression. Prior experience in RNA-seq experiments and analyses. Experience handling Arabidopsis plants. Experience in ATAC-seq. Skills and competencies: Advanced level of
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such as RNA-seq, ChIP-seq, ATAC-seq, MPRA, PC-HiC. Participates in the configuration and maintenance of laboratory instrumentation and equipment, including computers and servers, necessary for conduct
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, and multiomics experiments that probe islet cell transcriptional networks. Drive data acquisition and analysis (e.g., RNA-seq, ChIP-seq, functional assays) and co-author publications. Disseminate
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biology, and biochemical methods. Previous experience with RNA-seq data analysis, DNA encoded library design and screening, and structure-based design is also desirable. Highly motivated candidates
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cancer research, androgen receptor (AR) signaling, or RNA biology (mRNA splicing, polyadenylation, or antisense oligonucleotides). Experience with next-generation sequencing libraries/analysis (RNA-seq
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limited to, integrated cancer risk prediction model, cancer subtype discovery, genomic and epi-genomic data integration, single cell RNA-Seq data analysis, non-coding RNA data analysis, and other datasets
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University of Massachusetts Chan Medical School | Worcester, Massachusetts | United States | 2 months ago
, ideally single cell RNA seq or long read sequencing. •Demonstrated ability to conduct independent research and publish as first/co author. •Excellent communication, teamwork, and project management skills
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Interpretations, Data Management, Experimentation, Laboratory Operations, Laboratory Techniques, Molecular Biology Techniques, PCR Methods, Researching, Results Reporting, RNA-Seq, Scientific Writing, Statistical
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regulatory networks from single-cell genomics data, including RNA-seq and ATAC-seq, and to predict phenotypic outcomes of genetic perturbations. Our work primarily uses the model plant Arabidopsis. Funded