Summary

The Bioinformatics Analyst II will be responsible for leading, discovering, and developing a project that analyzes viral genomic signals from complex wastewater sequencing data sets for the purposes of stopping the next viral pandemic. The position will involve machine learning and data science to support research aimed at understanding the spatiotemporal interaction between pathogen profiles in wastewater and human health, under joint supervision of Drs. Maresso and Coarfa. Wastewater science is an evolving field with great potential. We have founded the Texas Environmental Biomonitoring group (TEXWEB) as part of a collaboration between the University of Texas School of Public Health in Houston and Baylor College of Medicine that aims to analyze wastewater from major cities across the state of Texas for viruses, their evolution and levels, and their relationship to disease of human populations. This work was recently acclaimed as the Breakthrough Medical Discovery of the Year by STAT news. The candidate should have deep computational genomic skills to examine read data from complex data sets and relate to viral reference data sets to understand the nature of genomic information. The successful candidate will receive training in analyses of massive multi-omics datasets data and be expected to participate in multiple projects involving design, implementation and integration of large-scale datasets and data processing pipelines. Successful models will be disseminated via open-source software packages. This is an opportunity to join a vibrant team of 20 plus scientists, health officials and clinicians pioneering the science of wastewater analysis.

Job Duties

• Analyze massive scale (hundred of thousand to million of datapoints) generated from pathogen profiles
• Advanced modeling of spatiotemporal interactions of pathogen profiles and human health using approaches including generalized linear models and deep learning, network analysis, and time series analysis
• Experience in microbiome or virome analysis is a plus, and experience with statistical analysis tools such as R or Python is recommended
• Runs and validates genome variation pipeline. • Reviews existing variation and propose improvements
• ests in-house bioinformatics tools and provides feedback to the software development team
• Performs manual annotation on genome variation data
• Interprets results from similarity searches and from mapping variation data on genomic coordinate systems
• Ensures that annotation of variation objectives are met as required by project or supervisor
• Uses various computational analyses and biological interpretation of results to review existing data and propose improvements.

Minimum Qualifications

• Bachelor's degree in Genetics, Biology, Bioinformatics, Biostatistics, Computational Biology, Computer Science, or a related field.
• One years of relevant experience.

Preferred Qualifications

• Masters or PhD degree in Genetics, Biology, Bioinformatics, Biostatistics, Computational Biology, Computer Science, or a related field.