Postdoctoral Scholar

Oregon Health & Science University • Portland, OR

1d • Hybrid

About The Position

This is a Postdoctoral Scholar position to conduct data-intensive translational research investigating the microbiome as an organ system and modifier of clinical outcomes in critical illness. The position focuses on sepsis, severe infections, and other intensive care unit syndromes, with emphasis on host-pathogen interactions and exposure-outcome relationships in large patient cohorts. The successful candidate will lead computational research projects integrating modern data science methods with microbiome science. This position requires demonstrated competence with structured electronic health record (EHR) data analysis, with the goal of developing mastery in advanced epidemiologic and biostatistical approaches applied to critical care populations. The research approach emphasizes "dry lab" activities including: high-throughput metagenomic data analysis, sophisticated biostatistical modeling of large clinical datasets, and machine learning applications to predict clinical outcomes based on microbiome and host factors. The postdoc will have opportunities to engage selectively in mechanistic validation studies and collaborate with wet-lab researchers to complement computational findings, though the primary focus (approximately 85-90% effort) will be on computational and data science methods. The ideal candidate has formal interdisciplinary training bridging microbiology/immunology and epidemiology, with proven ability to extract insights from complex datasets and translate findings into high-impact publications. The Division of Pulmonary, Allergy, and Critical Care Medicine (PACCM) provides expert diagnosis and care of patients with lung diseases in our Pulmonary Clinic; allergy, asthma, and immunologic disorders in our Allergy Clinic; and of critically ill patients in our Intensive Care Unit. In addition to our commitment to outstanding clinical care, PACCM is home to several outstanding research programs, conducting basic science research as well as clinical trials across a broad spectrum of subject matter. Our educational mission includes teaching on many levels, including but not limited to our fellowship programs in Pulmonary, Allergy, Sleep, and Critical Care Medicine. More information is available on our website: https://www.ohsu.edu/pccm.

Requirements

• PhD in Microbiology, Immunology, or closely related biological science AND prior training in Epidemiology, Biostatistics, or Public Health with epidemiologic methods concentration
• Minimum 3 years of research experience investigating microbiome and/or host-pathogen interactions using computational approaches
• Demonstrated experience analyzing structured electronic health record data or large clinical/epidemiologic datasets (n>500 subjects) with extraction and management of complex relational data
• Proven track record of leading -omics-level bioinformatic analyses resulting in peer-reviewed publications
• First-author publications in peer-reviewed journals (at least one in journal with Impact Factor >4)
• Experience with R, Python, or other high-level programming languages
• Advanced proficiency in statistical programming for data wrangling, analyses, and visualization; demonstrated ability to develop reproducible workflows and analysis pipelines
• Demonstrated competence in foundational biostatistical methods including regression modeling
• Basic molecular biology laboratory skills
• Excellent scientific writing skills with successful track record of fellowship applications
• Exercises judgment in taking independent action and seeks advice as necessary

Nice To Haves

• PhD in Microbiology, Immunology, or closely related biological science AND Master's degree in Epidemiology, Biostatistics, or Public Health with epidemiologic methods concentration
• Research experience specifically focused on sepsis, critical illness, infectious diseases, or intensive care populations
• Experience studying antimicrobial resistance or antimicrobial exposures in relation to microbiome ecology
• International research collaboration experience or cross-cultural research training
• Prior NIH training grant support (T32 or equivalent) or successful competitive fellowship funding (e.g. NIH F31, NSF GRFP)
• Publications in high-impact clinical or translational journals (AJRCCM, JAMA, Nature Medicine, etc.)
• Experience with database query language (e.g., SQL)
• Experience with animal models of infection or critical illness (observational or hands-on)
• Familiarity with prediction modeling and supervised machine learning
• Experience with observational causal inference methods (e.g., difference in differences, instrumental variables)
• Experience with version control software and reproducible research practices
• Familiarity with high-performance computing environments and parallelization

Responsibilities

• Design and execute computational research studies analyzing structured EHR data from large ICU cohorts (>1000 patients); extract, clean, and harmonize complex clinical data using SQL; develop and implement advanced biostatistical models examining microbiome as modifier of exposure-outcome relationships in sepsis and severe infections; apply modern data science methods including machine learning, causal inference approaches, and predictive modeling to integrated clinical-microbiome datasets.
• Perform -omics analysis using bioinformatic pipelines in R; integrate multi-omic data (metagenomics, clinical laboratory values, medication exposures, vital signs, microbiology cultures) to investigate host-pathogen interactions and microbiome as organ system; conduct sophisticated statistical analyses including multivariable regression, propensity score methods, mediation analysis, and survival modeling; develop data visualizations and interactive tools for exploratory analysis.
• Prepare manuscripts for high-impact peer-reviewed journals with focus on translational computational research; present research findings at national/international conferences; develop and submit competitive fellowship applications (NIH F32 or equivalent postdoctoral funding mechanisms); contribute substantively to R01 and other grant applications for the research program
• Attend and present at weekly lab meetings and division research conferences; participate in collaborative projects spanning clinical and computational research; advise trainees in data science and computational methods; engage selectively in wet-lab validation experiments based on research needs and training goals
• Other duties as assigned

Benefits

• Access to high-performance computing resources and large clinical datasets requires adherence to data security protocols and human subjects research regulations.

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: Ph.D. or professional degree