27 finite-element-analysis Postdoctoral positions at Duke University in United States
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progression and therapeutic response. You will be responsible for all aspects of the research: design of experiments, execution, analysis and reporting of data with guidance and mentoring from me at all stages
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analysis, and high-resolution chromatin mapping techniques. Opportunities also exist to extend these computational and genomic tools to mammalian systems in collaboration with departmental colleagues
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policy, urban planning, data science, environmental science, or a related field. • Strong quantitative skills in applied economic analysis (e.g., causal inference, econometrics, spatial equilibrium
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and immunoprecipitations RT-PCR and RNA analyses Cell transfections and selections RNA-seq and sequencing data analysis RNA modification profiling Lead one or more research projects investigating RNA
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related field prior to the start date. Responsibilities for both positions will include collaborating on the development of research protocols, data collection and analysis, budget and supply management
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to define and lead specific research objectives aligned with the funded aims. Responsibilities will include project management, coordination of data collection and analysis, manuscript preparation, and
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Bioinformatics expertise required for scRNAseq analysis. · Previous cell culture experience. · Perform molecular, cellular, biochemical and immunological analyses. · Optimize and troubleshoot experimental
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for the synthesis of lead compounds. • Perform detailed analysis and characterization of synthesized compounds using advanced techniques such as NMR, MS, and HPLC. • Collaborate closely with cross-functional teams
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Immunology, Data Science and/or related fields. MD/PhD with molecular biology research experience. Must have experience with analyzing omics data. Familiarity or direct experience with analysis of 10x Genomics
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candidate will work directly with experimental scientists within a wet lab setting to facilitate the management, analysis, and visualization of the mass spectrometry-based proteomic data generated in our