Summary

Baylor College of Medicine invites applications for an innovative and highly interdisciplinary Postdoctoral Associate position focused on decoding how infection leaves durable epigenetic and transcriptional scars that shape long term immunity, inflammation, and lung repair.

This position is ideal for scientists excited to combine single cell transcriptomics and chromatin accessibility, CRISPR based gene and epigenetic perturbation, and deeply phenotype human cohorts to define causal mechanisms driving post infectious and post tuberculosis lung disease.

Postdoctoral Associate will need to implement single cell transcriptomics and epigenomic (scRNA-seq and scATAC, CITE-seq, Perterb-Seq, Cut and Run) on samples collected from participants who have had sepsis, pneumonia, and tuberculosis. Functional/ mechanistic validation of regulatory elements and gene networks controlling inflammation, immune memory, and tissue repair will be implemented using CRISPR inhibition and activation (CRISPRi/a) in primary immune cells and lung organoids. They are expected to be the first author on high-impact manuscripts that can form the basis for K-level or equivalent fellowship applications, or transitions to faculty. The position is optimal for applicants wanting to a) link multi-omic patient results to mechanistic, causal biology; b) eager to integrate wet-lab experiments with advanced bioinformatic analysis; c) excited to work at the interface of human cohorts with robust epidemiologic and long-term outcomes with experimental systems; d) aim to transition to independent investigator roles in academia or industry. 

Job Duties

• Implements measurements of inflammation, host immunity, and cellular metabolism from ex vivo PBMCs and plasma from patients recovering from sepsis, pneumonia, or Tuberculosis.
• Implements and analyzes single cell RNA-Sequencing, single cell ATAC-Seq, CITE-Seq, Cut and Run, as well as bulk RNA-Seq, Proteomics, and Metabolomics datasets, generated from pneumonia tuberculosis patient cohorts with rich clinical data.
• Works on advanced modeling of post tuberculosis lung disease risk using approaches including generalized organoid models, and linear models and deep learning.

Minimum Qualifications

• MD or Ph.D. in Basic Science, Health Science, or a related field.
• No experience required.

Preferred Qualifications

• Ph.D. in Biology, Immunology, Computer Science, or Bioinformatics.
• Strong background in statistics, and familiarity with large data sets such as metabolomics or epigenetic sequencing.
• Experience in epigenetics or gene regulation is a plus.
• Experience with statistical analysis tools such as R or Python is recommended (successful candidates will be expected to pass a basic programming test in R or Python).
• Excellent written and verbal English skills, strong communication and interpersonal skills, and be capable of working with large collaborative teams.