Data Scientist Infectious Disease

The faculty in MBM are united by a common interest in the biology of microbes (bacteria, fungi, viruses, and parasites) and the effects of microbes on human and animal hosts. The microbial pathogens program studies microorganisms that cause infectious diseases, with emphasis on rigorous analysis of these pathogens and the immune responses that restrict them. Program investigators direct detailed studies of pathogenic bacteria, viruses, fungi and eukaryotic parasites. Research topics include structural analysis of virus entry, viral evolution, viral oncogenes, intracellular bacterial pathogens, microorganisms that cause diarrhea and pneumonia, intracellular signaling, pathogen adhesion, genetic determinants involved in susceptibility to infection, vaccine development, and identification of novel antimicrobial agents.

Designs, organizes, and carries out computational and data science research in a highly quantitative, multidisciplinary research laboratory focused on tuberculosis and bacterial biology and combination therapy design. Works collaboratively with the PI and other lab members on data analysis strategy, computational method development, and problem-solving across lab projects. Collaborates with experimental and computational lab members to develop and apply novel quantitative methods, including image analysis pipelines, machine learning models, and statistical methods for analyzing single-cell and drug combination data. Works with the PI to lead and review data analysis projects, including quantitative image analysis, computational modeling of bacterial cell biology, and analysis of drug combination datasets. Develops and implements novel computational and statistical methods tailored to the lab's research questions. Contributes original research published in peer-reviewed journals and works collaboratively with lab members to develop, edit, and submit manuscripts. Participates in project design and written and visual communication of lab projects for presentations, papers, and grant applications. Participates in teaching, research, and mentoring, including providing guidance to graduate students and labmates.

Research in the lab is highly multidisciplinary and quantitative, requiring expertise in computational modeling, quantitative image analysis, statistical learning, and the application of AI/ML algorithms to biological data (e.g., single-cell imaging data, high-throughput drug combination data). We are looking for someone who will take ownership of computational strategy and push these methods forward. Opportunities also exist for candidates who wish to engage in wet lab and/or BSL-3 experimental work, though prior experimental experience is not required.