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Open Postdoctoral position, faculty mentor Nilam Ram

Important Info

Faculty Sponsor (Last, First Name): 
Ram, Nilam
Other Mentor(s) if Applicable: 
David Rehkopf
Stanford Departments and Centers: 
Epidemiology and Population Health
Postdoc Appointment Term: 
Appointment Start Date: 
How to Submit Application Materials: 

Please send materials to A Garron at

Does this position pay above the required minimum?: 
Yes. The expected base pay range for this position is listed in Pay Range field. The pay offered to the selected candidate will be determined based on factors including (but not limited to) the qualifications of the selected candidate, budget availability, and internal equity.
Pay Range: 
74,650 - 85,000

In collaboration with The Stanford Center on Longevity we seek Postdoctoral Scholars for a full time, 1 year (potentially renewable)  appointment in the Department of Psychology or School of Medicine at Stanford University. The aim of the post-doc is to study how innovations in AI, especially adaptation of Large Language Models (LLMs) architectures for time-series data, can be used in study of aging, health span, and longevity.
The postdoctoral scholar would be advised by Prof. Nilàm Ram (Psychology & Communication, The Change Lab @ Stanford), and Prof. David Rehkopf (Epidemiology and Population Health).

● Dr. Ram’s group specializes in longitudinal research methodology and lifespan development – particularly in how longitudinal study designs contribute to our understanding of human development and psychological change processes. The lab develops and uses novel longitudinal methods to articulate and examine how individuals change over time (and with age), and how various individual characteristics, contextual factors, and events influence those trajectories.
● Dr. Rehkopf’s is the Director of the Stanford Center for Population Health Sciences where he and his group develop understanding of how corporate and governmental decisions contribute to health inequalities, and how both the public and policy makers can support new strategies for promoting health and well-being.
Our labs are combining in this project to propel discovery of how recent advances in AI can promote healthy aging and longevity around the world. The general aim of the work is to study how new LLM-type modeling architectures can be applied to large longitudinal data repositories of multimodal data (such as the Health and Retirement Study; HRS) to obtain detailed and accurate predictions of individual’s health trajectories through adulthood and old age, and how those developmental trajectories differ (and are similar) across contexts.
Planned projects:
The planned research will focus on design of models and application to a set of large, well-curated, high value datasets that characterize a large number of individuals’ developmental trajectories across adulthood and old age.
The modeling and research has two key aims. Aim 1. Develop strategies for how LLMs can be used to obtain richly informative embeddings for developmental/aging trajectories from different kinds of data sources. Aim 2. Enhance capacity to accurately predict longevity and health span and, through those prediction, inform a variety of individual-level and community-level policy making efforts.  The results of the analysis and investigations will inform design and implementation of next-generation longitudinal studies and applications.
Complementary projects on longitudinal AI modeling, design of longitudinal data, and study of developmental processes and aging are expected and encouraged. Collectively, the group has a substantial collection of longitudinal data repositories available for innovative analysis and is eager to expand the possibilities for modeling multidimensional change at multiple time scales and levels of analysis.

Required Qualifications: 

Highly motivated postdoctoral researcher with extensive experience as follows;

  • Ph.D. (or expected completion by Fall 2024) in computer science, statistics, a computational social science or related discipline.
  • Demonstrated interest in large language models and study of change.
  • Substantial experience with transformer and other machine learning models (especially neural network models, time-series models) and coding in python and R.
  • Strong collaborative skills and ability to work well in a complex, multidisciplinary environment across multiple teams, with the ability to prioritize effectively.
  • Being highly self-motivated to leverage the distributed supervision structure.
  • Must be able to work well with academic and industry/foundation personnel. English language skills (verbal and written) must be strong.
  • Eager to contribute to a vibrant group of faculty, post-docs, and students coalescing around longitudinal modeling issues.
Required Application Materials: 
  • Cover letter (2 pages)
  • CV
  • Copies of two research papers that demonstrate research agenda
  • Contact information for two references 


Stanford is an equal opportunity employer and all qualified applicants will receive consideration without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other characteristic protected by law.