Postdoctoral Research Associate - AI, LLMs, Multimodal & Foundation Models, and Agentic AI

FLSA Status

Job Summary

A Postdoctoral Research Associate position is immediately available as a co-hire between the Department of Pharmacy Practice, Purdue University College of Pharmacy (Indianapolis campus), and the Department of Biostatistics and Health Data Science, Indiana University School of Medicine. The fellow will work in a highly collaborative environment with Purdue, Indiana University, and Regenstrief Institute investigators to develop and apply advanced AI methods for biomedical discovery, clinical research, and precision health.

This position is ideal for candidates seeking advanced training and leadership opportunities at the intersection of artificial intelligence, large language models (LLMs), multimodal biomedical data, foundation models, agentic AI and AI agent development, and translational health data science.

Research Areas: The postdoctoral fellow will contribute to projects in one or more of the following areas:

• Large language models and generative AI for biomedical and clinical applications

• Biomedical and clinical foundation models, including pretraining, adaptation, and evaluation

• Multimodal AI integrating structured EHR data, clinical text, imaging, omics, and other biomedical data

• Representation learning, self-supervised learning, and transfer learning for health data

• Agentic AI and AI agent development for clinical and biomedical research, including tool use, reasoning, planning, memory, orchestration, and multi-agent workflows

• Biomedical knowledge graphs, retrieval-augmented generation, and knowledge-enhanced AI

Primary Responsibilities: The Postdoctoral Research Associate is expected to lead and contribute to independent and collaborative research projects, including but not limited to:

• Developing and evaluating LLMs, multimodal AI models, biomedical foundation models, and agentic AI systems

• Integrating and analyzing large-scale multimodal biomedical datasets, including EHRs, clinical text, imaging, omics, medication, and other real-world health data

• Designing pretraining, fine-tuning, instruction-tuning, RAG, multimodal learning, and AI-agent development pipelines

• Developing AI-enabled methods for clinical phenotyping, prediction, knowledge discovery, precision therapeutics, and intelligent research automation

• Leading manuscript preparation and contributing to grant proposals, open-source tools, and scientific dissemination

• Collaborating with multidisciplinary teams across clinical, biomedical, informatics, and AI research

Core Qualifications

1. Ph.D. or equivalent degree in a related field, such as computer science, artificial intelligence, biomedical informatics, health data science, computational biology, biomedical engineering, biostatistics, or a related discipline

2. Demonstrated research experience in one or more of the following areas: LLMs; generative AI; agentic AI or AI agent development; foundation models; multimodal learning; NLP; deep learning; self-supervised learning; representation learning; or biomedical AI

3. Strong programming skills in Python and experience with modern deep-learning frameworks such as PyTorch, TensorFlow, or related tools

4. Experience working with large-scale biomedical or healthcare datasets; experience with EHR, clinical text, imaging, omics, knowledge graphs, or other multimodal data is highly desirable

5. Strong written and oral communication skills, with evidence of scholarly productivity, including peer-reviewed publications, conference papers, or equivalent research outputs

6. Ability to work effectively in multidisciplinary teams and independently drive innovative AI research projects

Application Materials

1. Curriculum Vitae

2. Cover letter describing research interests, relevant AI/LLM/agentic AI experience, and career goals

3. Contact information for 3-4 references

Contact

Serena Guo, MD, PhD ( serena.guo@purdue.edu ) Yi Zhao, PhD ( yz125@iu.edu )

Purdue University is an equal opportunity/equal access university.