Postdoc, UT Tyler

  • Scientist
  • Technician/Research Asst.
  • Using computational biology to identify biomarkers of ARDS
  • Applications have closed

Dallas, Texas, USA


Description:

The individual will identify and implement computational solutions to research problems related to ARDS and sarcoidosis. They will be responsible for taking on highly interdisciplinary projects and key functions in this endeavor, including applying state-of-the-art open-source software for basic and advanced analysis of next-generation sequencing data (scRNAseq) and proteomics data. They will also analyze epigenetics and transcriptomic datasets.


Responsibilities:

-Analyze omics data using online available R or Python packages for omic data processing.

-Process omics data for clustering and function annotations to identify new endotypes.

-Identify biomarkers from human and microbial proteins using R or Python.

-Correlate identified molecular endotypes with phenotypes

-Collaborate with wet lab scientists to prepare cell/tissue samples for LC-MS and scRNAseq

-Prepare reports, charts, and graphs for presentations and publications

-Maintain detailed records of computational code and processes

-Manage omics and metadata

-Search and evaluate scientific literature in support of research projects

-Prepare manuscripts, progress reports, and grant applications.


Qualifications/Preferred Skillsets:

-Programming Languages: Python, R, and others

-Analytical: critical thinking, data modeling, problem-solving, troubleshooting

-Demonstrated ability working with open-source bioinformatics software

-Experience in bioinformatics analysis of RNA-Seq and proteomics data

-A record of taking initiative to solve problems and working to high-quality standards Attention to detail and accurate record keeping

-Ability to multitask, work and learn independently, and be self-motivated Publication record: demonstration of productivity in omics data analysis

-Masters’ or Ph.D. in Bioinformatics, Biostatistics, Computational Biology, Computer Science, Genetics, Biology or a related field. Multi-disciplinary trainee, with a working experience of 3+ years, is preferred.


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