This position involves the design, execution, and analysis of quantitative disease simulation models, including state-transition (Markov) models, discrete event simulations, differential equations, and agent-based modeling approaches. The candidate will integrate epidemiologic and clinical data from national datasets and the scientific literature into robust computational frameworks.

Specific duties include:

• Design, develop, and validate computational disease models using state-transition, discrete event, differential equation, and agent-based modeling methodologies.
• Implement simulation models in Python, R, C, C++, or other scientific programming environments.
• Integrate model parameters using national datasets for incidence, mortality, and related epidemiologic outcomes.
• Conduct structured literature reviews to inform model inputs, assumptions, and validation.
• Apply principles of diagnostic test accuracy, including ROC analysis.
• Perform statistical analyses, including multivariable regression and time-to-event methods, as needed to support modeling.
• Develop and maintain databases of model inputs, outputs, and analytical results.
• Prepare manuscripts, abstracts, and presentations for peer-reviewed journals and scientific conferences.
• Present regular progress updates to the Principal Investigator and collaborate with a multidisciplinary research team.
• Mentor junior researchers, including Postdoctoral Fellows and research assistants, as appropriate.

Minimum Qualifications:

1. Ph.D. in decision sciences, industrial engineering, epidemiology, biostatistics, mathematics, engineering, computational sciences, or a related quantitative field.
2. Demonstrated expertise in simulation modeling methodologies, including Markov/state-transition, discrete event, differential equation, and/or agent-based modeling.
3. Strong foundation in probability theory and statistical methods.
4. Proficiency in scientific programming (Python, R, C, C++, or equivalent).
5. Experience conducting systematic literature searches using PubMed/MEDLINE, Embase, or similar databases.
6. Excellent written and oral communication skills in a scientific context.
7. Strong organizational skills for managing complex datasets and multi-component modeling projects

Preferred Qualifications:

• Experience in decision analytic modeling or economic evaluation.
• Knowledge of epidemiologic methods and health outcomes research.
• Prior involvement in modeling cancer or oral lesion progression.
• Experience mentoring trainees and collaborating within multidisciplinary research teams.

This position involves the design, execution, and analysis of quantitative disease simulation models, including state-transition (Markov) models, discrete event simulations, differential equations, and agent-based modeling approaches. The candidate will integrate epidemiologic and clinical data from national datasets and the scientific literature into robust computational frameworks.

Specific duties include:

• Design, develop, and validate computational disease models using state-transition, discrete event, differential equation, and agent-based modeling methodologies.
• Implement simulation models in Python, R, C, C++, or other scientific programming environments.
• Integrate model parameters using national datasets for incidence, mortality, and related epidemiologic outcomes.
• Conduct structured literature reviews to inform model inputs, assumptions, and validation.
• Apply principles of diagnostic test accuracy, including ROC analysis.
• Perform statistical analyses, including multivariable regression and time-to-event methods, as needed to support modeling.
• Develop and maintain databases of model inputs, outputs, and analytical results.
• Prepare manuscripts, abstracts, and presentations for peer-reviewed journals and scientific conferences.
• Present regular progress updates to the Principal Investigator and collaborate with a multidisciplinary research team.
• Mentor junior researchers, including Postdoctoral Fellows and research assistants, as appropriate.

Minimum Qualifications:

1. Ph.D. in decision sciences, industrial engineering, epidemiology, biostatistics, mathematics, engineering, computational sciences, or a related quantitative field.
2. Demonstrated expertise in simulation modeling methodologies, including Markov/state-transition, discrete event, differential equation, and/or agent-based modeling.
3. Strong foundation in probability theory and statistical methods.
4. Proficiency in scientific programming (Python, R, C, C++, or equivalent).
5. Experience conducting systematic literature searches using PubMed/MEDLINE, Embase, or similar databases.
6. Excellent written and oral communication skills in a scientific context.
7. Strong organizational skills for managing complex datasets and multi-component modeling projects

Preferred Qualifications:

• Experience in decision analytic modeling or economic evaluation.
• Knowledge of epidemiologic methods and health outcomes research.
• Prior involvement in modeling cancer or oral lesion progression.
• Experience mentoring trainees and collaborating within multidisciplinary research teams.