Summary

The Translational Imaging Group (TIGR) lab seeks a postdoctoral researcher to work on innovative projects integrating preclinical and clinical data with AI and ML. This role includes partnering with AI/ML/NLP teams, developing algorithms, and deriving insights to advance groundbreaking research. The Postdoctoral Associate will also publish papers and present research locally and nationally.

Baylor College of Medicine typically follows similar to the NIH stipulated stipend guidelines for Postdoctoral Associates.

Job Duties

• Collaborates with computational teams to design, implement, and validate AI, ML, and NLP models tailored to complex healthcare challenges.
• Conducts exploratory data analysis, data curation, and statistical modeling on high-dimensional preclinical and clinical datasets.
• Generates domain-specific clinical annotations to train and benchmark large language models (LLMs) and clinical NLP algorithms.
• Leverages clinical and biological domain knowledge to ensure models are methodologically sound, clinically meaningful, and generalizable.
• Partners across Baylor College of Medicine (BCM) and Texas Children’s Hospital (TCH) to debug, validate, and deploy models in translational and clinical informatics pipelines.
• Leads the drafting and preparation of high-impact peer-reviewed manuscripts, grant contributions, and conference presentations.
• Adheres rigorously to institutional review board (IRB) protocols, HIPAA regulations, and ethical guidelines governing patient data in research.
• Maintains continuous awareness of emerging advancements in bioinformatics, deep learning architectures, and healthcare data science.
• Collaborates with data scientists, machine learning engineers, and clinical partners to design, implement, and validate innovative algorithms.
• Ensures compliance with ethical guidelines governing the use of patient data in research and development and maintains effective collaboration with relevant stakeholders.
• Performs other job-related duties as assigned.

Minimum Qualifications

• MD or Ph.D. in Basic Science, Health Science, or a related field.
• No experience required.

Preferred Qualifications

• Ph.D. or M.D. in Biomedical Informatics, Computer Science, Data Science, Biomedical Engineering, Health Sciences, or a closely related quantitative or life science field.
• Knowledge in biostatistics, machine learning fundamentals, and study design.
• Practical experience working with medical imaging, electronic health records (EHR), or preclinical bioinformatics data.
• Proficiency in common scientific programming environments (e.g., Python, R) and relevant ML/NLP libraries.
• Demonstrated track record of scientific writing (e.g., peer-reviewed publications, preprints, or substantial thesis work).
• Strong communication skills and enthusiasm for bridging the gap between computational data science and clinical medicine.