Associate Scientist, Postdoctoral Fellow - AI in Infectious Disease Modeling

Be a part of the legacy: Postdoctoral Research Fellow Program

Our Research Laboratories’ Postdoctoral Research Fellow Program aims to be a best-in-industry program for industrial postdoctoral researchers, designed to provide you with an academic focus in a commercial environment. With the resources, reach, and expertise of a large pharmaceutical company, postdoctoral researchers will be positioned to excel in an institution committed to breakthrough innovation in research and discovery.
 

Position Overview:

This position is in the vaccine modeling team of the Health Economic and Decision Sciences (HEDS) department withing Biostatistics and Research Decision Sciences (BARDS) at our Research Laboratories. BARDS HEDS provides strategic and analytic modeling expertise to measure and quantify the value of our company's products.

As part of an agile team, the fellow will develop and support a seamless mechanism of coordination and collaboration in a dynamic area of interdisciplinary research. Collaboration with modeling scientists, data scientists, IT partners, subject matter experts and strategically leveraging generative AI to automate model extraction and facilitate rapid, intelligent adaptation across diverse settings, this project will profoundly enhance model transparency, dramatically accelerate deployment, and improve consistency in global health decision-making. This innovative approach promises to transform the efficiency and impact of health economic evaluations in infectious disease preparedness and response.

At BARDS, we value diversity and inclusion in our working environment where employees are enabled to develop and contribute.

Responsibilities include but are not limited to:

Reporting under a Senior Director within the Vaccines team within HEDS, the fellow is expected to:

Build a robust corpus of health economic infectious disease models using AI-assisted systematic review techniques,

Utilize advanced LLMs to precisely extract infectious disease model type, structure (states, transitions), and parameters (costs, utilities, probabilities),

Implement an intelligent AI reasoning layer to evaluate consistency, completeness, and scientific validity across extracted models,

Employ generative AI to propose new model structures, dynamically guided by disease-specific needs and prior evidence,

Apply the developed pipeline to critical case studies (e.g., RSV vaccine modeling) and rigorously compare AI-generated models with existing approaches,

Develop excellent working relationships within partner across our Research Laboratories; ensure effective cross-functional collaboration across teams,

Collaborate externally and solicit input from appropriate stakeholders and adopt latest techniques from relevant published literature, and

Disseminate key research findings/methodology via scientific presentations at congresses and publications in scientific journals.

Education Minimum Requirement:  

Candidates should currently hold a PhD OR will receive a PhD by start of employment in Computer Science, Mathematics, Statistics, or a closely-related quantitative field.

Required Experience and Skills:

Previous experience with large language models (LLMs), natural language processing (NLP), or machine learning,

Strong programming skills in Python and/or C++, with experience in model fitting, simulation, and data extraction workflows,

Previous experience working on interdisciplinary research projects and/or working within interdisciplinary teams,

Ability to gather, organize, and synthesize complex information in order to draw conclusions and make recommendations,

Strong creative problem solving skills,

Strong interpersonal, networking, presentation, and communication skills, and

Ability to communicate effectively in English in both verbal and written formats.

Preferred Experience and Skills:

Experience in prompt engineering, fine-tuning, or evaluating large language models, and

Previous experience of health economic modeling and cost-effectiveness analysis for infectious disease.