24 multiple-sequence-alignment Fellowship research jobs at Nature Careers in United States
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unpublished, preliminary data to support multiple clinically relevant projects through either line of investigation. Although the lab has recently focused on macrophage biology, we also have expertise and can
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computing environments. Experience with single cell sequencing or spatial omics data strongly preferred. Strong publication record and written communication skills. Desired Qualifications* Expert in graph
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(http://vanallenlab.dana-farber.org/) to work on the analysis of new datasets generated in the context of multiple clinically oriented cancer sequencing projects in order help advance efforts
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manuscript writing. Additionally, pre-planning projects, RNA isolation, quality control, qPCR, protein assays and quantification, Western Blot, cell culture, single cell and next-gen sequencing experiments
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and (bio)statistics, biology or medicine. Applicants must have a proven publication record. Prior experience with handling massively parallel sequencing data is considered an advantage. Postdocs will
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of patient-derived cell lines and mouse models, and cutting-edge technologies such as CRISPR, and multi-omics approaches including single-cell RNA-sequencing, epigenomics, proteomics, metabolomics and
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sequencing (NGS) and bioinformatic analysis, as well as protein display technologies and antibody engineering, is highly desirable. Laboratory research is supported by multiple funding sources, including
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Nucleome Program, 1000 Genomes Project, Roadmap Epigenomics, ENCODE, Impact of Genomic Variation on Function (IGVF), BRAIN Initiative, Human Pangenome Project, and the Alzheimer’s Disease Sequencing Project
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for Pulmonary and Vascular Biology at UT Southwestern Medical Center (Dallas TX, USA) is seeking two self-motivated postdoctoral fellows to join the team to lead multiple NIH-funded projects. The Shaul-Mineo Lab
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acquire additional skills and be involved in the analysis of single-cell RNA Sequencing, WES, and in situ transcriptomics data. Trainees will present their findings at regular lab meetings, departmental