Postdoctoral Associate – Specialist

Summary

We are looking for a highly motivated Post-Doctoral Associate to join the Li Lab, with a focus on developing clinically grounded large language model (LLM) systems to advance patient safety, documentation automation, clinical trial matching, and interpretable phenotyping in oncology and thrombotic disorders. The ideal candidate will have expertise in machine learning (ML) and natural language processing (NLP), particularly in applying LLMs to complex, unstructured clinical data.

The successful candidate will become part of our dynamic research team, focusing on the complex relationship between cancer and thrombosis. This role will utilize advanced computational techniques, including multi-modal clinical, imaging, and omics data, to uncover new insights and develop predictive models. Through interdisciplinary collaboration, the aim is to deepen our understanding of thrombotic complications in cancer patients and contribute to the development of innovative strategies for risk assessment, prevention, and treatment.

• Conducts Research:
  Conducts in-depth literature reviews and stays abreast of the latest advancements in cancer-related thrombosis research, machine learning, and NLP.
• Leads the development and application of large language model (LLM) and machine learning (ML) approaches to extract, structure, and interpret unstructured clinical data, such as free-text notes.
• Collaborates with clinicians and researchers to integrate multi-omics data and clinical variables for comprehensive analysis and interpretation.
• Analyzes large-scale biomedical data (e.g., EHRs, clinical trials, omics, and imaging) using advanced computational pipelines to uncover clinically meaningful insights into cancer and thrombosis.
• Implements predictive models to identify cancer patients at high risk of thrombosis, leveraging both traditional statistical approaches and cutting-edge machine learning algorithms.
• Implements and refines NLP pipelines that transform free-text clinical documentation into structured, actionable data for use in electronic health records (EHRs) and clinical decision support systems.
• Employs interpretable and robust modeling approaches – such as rule refinement, clustering, and causal inference – to support disease subtyping, progression prediction, and treatment effect estimation.
• Collaborates closely with oncologists, hematologists, bioinformaticians, and computational biologists to contextualize findings and validate hypotheses.
• Participates in research meetings, seminars, and workshops to exchange ideas and foster interdisciplinary collaboration.
• Contributes to manuscript preparation and grant writing efforts to disseminate research findings and secure external funding.

• Ph.D. in Chemistry, Computational Sciences, Computational Biology, Structural Biology, Computer Science, Bioinformatics, Statistics, or related disciplines. May also include Ph.D. in Biology or Biomedical Sciences in combination with an M.S. or extensive multidisciplinary experience in one of the above quantitative fields.

• Ph.D. in Biomedical Informatics Proficient in Python and R programming languages.
• Prior clinical or research experience in hematology and oncology.
• A strong record of first or senior-position peer-reviewed publications.
• Prior experience in algorithm development, especially with software packages and libraries commonly used in informatics, machine learning, and NLP.

Baylor College of Medicine is an Equal Opportunity/Affirmative Action/Equal Access Employer.