Postdoctoral Associate in Population Health Sciences

Position Summary

The position is responsible for analyzing and interpreting data using advanced mathematical and computational models to support project-related research on the biomedical and behavioral dimensions of population dynamics for both communicable and non‑communicable diseases.

• Develops mathematical models describing disease dynamics and the impact of interventions.
• Formulates hypothesis independently and performs comprehensive data analysis using mathematical modeling across multiple research projects.
• Develops, validates, and maintains deterministic, stochastic compartmental, and individual-based models to support disease-related investigations.
• Partners with principal investigators and multidisciplinary research teams to co‑develop study designs, and define robust computational plans aligned with research aims.
• Interprets modeling outcomes and prepares reports, visualizations, and scientific manuscripts summarizing findings and contributes to manuscripts and grant submissions.
• Writes and publishes articles in peer‑reviewed journals that highlights research findings.
• Presents research results in internal meetings and external scientific conferences.
• Maintains accurate documentation of analytical methods, results, and project progress.
• Researches articles with respect to the assigned study, keeps up to date with the latest developments and identifies areas of improvement.
• Guides team members on the effective use of tools, methods and workflows as required.
• Performs other duties as assigned.

• PhD

Ph.D. in Mathematical Epidemiology, Applied Mathematics, Theoretical Physics, or a related quantitative discipline with relevant post‑qualification experience.

• Extensive knowledge of mathematical modeling frameworks, including deterministic and stochastic approaches.
• Extensive knowledge of quantitative methods including differential and integral equations, nonlinear dynamics, probability theory, and stochastic processes.
• Expert at scientific computing, including programming, numerical analysis, symbolic and logical analysis, Monte Carlo simulations, and computer graphics.
• Extensive knowledge in use of scientific computing tools such as MATLAB, Mathematica, and Berkeley Madonna, and statistical software including SPSS and R.
• Advanced problem‑solving and analytical skills with an eye for detail.
• Excellent organizational skills with the ability to manage multiple priorities in demanding time frames.
• Proficient in MS Office Suite (Word, Excel, PowerPoint, Outlook).