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in the lab. · Computational biology: Perform bioinformatic analyses, including RNAseq analysis, genome sequence alignment, phylogenetic tree creation, and protein structure prediction
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communication skills for interacting with colleagues and stakeholders. Advanced epidemiology training/work experience preferred (e.g., causal methods, longitudinal analysis, life course methods, missing data
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. Designs and writes programs to perform analysis and produces written reports on results. Collects pilot data to help support new and competing renewal applications. Supervises undergraduate and graduate
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biology and cell biology techniques such as nucleic acid extraction and manipulation, PCR, RT-PCR, Western blot analysis, immunostaining, cell culture, cell transfections, cell sorting. Performs genomic
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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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recommends schedules. Presents research findings at local and national scientific conferences. Writes technical publications and reports. Designs and writes programs to perform analysis and produces written
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biology. Experience with single-cell RNA sequencing data analysis, basic scripting in R or Python, and integration of multi-omic datasets is considered an advantage. Candidates should also possess a strong
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Department Specific Functions Design, synthesize, and characterize metal chelating nanoparticles and integrate them into nano wipe substrates Conduct experimental studies to evaluate the efficacy of nano wipes
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immunofluorescence. Familiarity with rodent models of diabetes, data analysis, and interpretation of complex biological datasets is essential. The candidate should demonstrate the ability to work independently
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Experience: Strong background in computational modeling, cancer genomics, and high-throughput data analysis. Communication Skills: Excellent written and verbal communication skills for effective collaboration