Post-Doc

Case Western Reserve University; Cleveland (OH); On-site; USD 52,000 - 68,000; Full time

Job summary

A prestigious university in Cleveland is seeking a highly motivated postdoctoral scholar to join the Clinical Application and Statistical Learning Lab. This role will focus on developing statistical methods for causal inference and data integration in large-scale healthcare data. The position requires a doctorate in a quantitative field, proficiency in statistical programming using R or Python, and at least two years of relevant experience. Responsibilities include leading data analyses, engaging in collaborative research, and co-leading manuscript preparation.

Qualifications

• Doctorate in statistics, biostatistics, mathematics, data science or related field.
• At least two years of experience in biostatistics or related areas.
• Proficiency in statistical programming, preferably R or Python.
• Excellent communication skills.

Responsibilities

• Develop and implement statistical methods in causal inference and data integration.
• Lead data analyses for NIH funded grant projects.
• Conduct simulation studies to support new grant submissions.
• Engage in collaborative research in various diseases.
• Co-lead manuscript preparation and supervise students.

Skills

Statistical programming

Data integration

Causal inference

Longitudinal data analysis

Survival analysis

Risk prediction

High-dimensional data analysis

Education

Doctorate in statistics, biostatistics, mathematics, data science or related quantitative field

Tools

R

Python

Job description

Overview

We are seeking a highly motivated postdoctoral scholar to join the Clinical Application and Statistical Learning (CASTLE) Lab led by Dr. Ming Wang at Case Western Reserve University. The position is available immediately and will focus on statistical method development in causal inference and data integration, with applications to large-scale healthcare data, as well as collaborative biomedical and human health research.

Case Western Reserve University, Department of Population and Quantitative Health Sciences seek a qualified candidate to fill a postdoctoral position. This is a one-year appointment with the possibility of renewing for an additional year.

Responsibilities and Duties

1. Develop and implement statistical methods in causal inference and data integration using large-scale healthcare databases and related data sources.
2. Lead data analyses for NIH funded grant projects, and prepare analytic reports aligned with specific aims and project milestones.
3. Conduct simulation studies and preliminary analyses to support new grant submissions (e.g., R01/R21/R03), including evaluation of power and operating characteristics for proposed methods.
4. Engage in collaborative research using clinical trials and observational data in cancer, cardiovascular, kidney, and neurodegenerative diseases, with emphasis on decision making and value-based care.
5. Co-lead manuscript preparation, contribute to grant writing, supervise or mentor graduate students and research staff, and help build reproducible analysis pipelines to support future projects and student onboarding.

Minimum Qualifications

• Doctorate in statistics, biostatistics, (applied) mathematics, data science or a closely related quantitative field.
• At least two years of experience (can include doctoral or postdoctoral work) in biostatistics or related areas, with expertise in one or more of the following: causal inference, data integration, longitudinal data analysis, survival analysis, risk prediction, large-scale or high-dimensional data analysis.
• Proficiency in statistical programming, preferably in R and/or Python; experience with high-performance or parallel computing is strongly preferred.
• Excellent written and oral communication skills necessary.
• Must be eligible to work in the United States.

How to Apply

Review of applications will begin on Jan 1, 2026. Send a cover letter of application, curriculum vitae, transcripts, two current letters of recommendation, and writing samples to the Interfolio posting.

Link for applicants to apply: https://apply.interfolio.com/178309