Postdoctoral Fellow — Pandemic Modeling & Forecasting

The University of Texas at Austin; Utah; On-site; USD 63,000 - 77,000; Full time

Benefits offered by this job

Full benefits package

Job summary

The Meyers Lab and epiENGAGE at The University of Texas at Austin invite outstanding postdoctoral scholars to contribute to modeling the spread, surveillance, forecasting, and control of influenza and other infectious diseases. Work under Prof.

Lauren Ancel Meyers, applying quantitative methods to improve outbreak preparedness. Ideal candidates have strong quantitative training in modeling, statistics, ML/AI, or operations research, with preference for infectious disease epidemiology knowledge.

Qualifications

• PhD completed within the last 3 years in a quantitative science, statistics, computer science, mathematics, engineering, or a related discipline.
• Ability to work quickly on time-sensitive analyses and collaborate with an interdisciplinary team.

Responsibilities

• Lead the development and analysis of mathematical models to support early detection, forecasting, and mitigation of infectious disease outbreaks.
• Contribute to the development of outbreak modeling tools and training resources for public health professionals.
• Disseminate this work through engagements with scientists and public health professionals, and through peer-reviewed publications.

Skills

Quantitative training

Statistics

Python

Epidemiology knowledge

Education

PhD in a quantitative science

Tools

R

Python

Mathematical modeling tools

Job description

The Meyers Lab and epiENGAGE at The University of Texas at Austin invite outstanding postdoctoral scholars to contribute to modeling the spread, surveillance, forecasting, and control of influenza and other infectious diseases. Work under Prof.

Lauren Ancel Meyers, applying quantitative methods to improve outbreak preparedness. Ideal candidates have strong quantitative training in modeling, statistics, ML/AI, or operations research, with preference for infectious disease epidemiology knowledge.