Postdoctoral Associate - Specialist

Baylor College of Medicine - Houston, TX

Full-time - Entry Level

Houston, TX

Educational Services

About the position

The Postdoctoral Associate - Specialist position at Baylor College of Medicine focuses on advanced computation, particularly in machine learning and AI, to analyze large clinical data sets. The role involves collaboration with a multidisciplinary team to enhance surgical outcomes through innovative research and the development of predictive algorithms. The position offers training in data science and opportunities to contribute to high-impact scientific publications.

Responsibilities

• Plans, directs, and executes specialized research projects related to surgical outcomes using data science techniques.
• Designs and implements machine learning models, focusing on predictive algorithms for surgery-related outcomes.
• Develops and applies advanced statistical methodologies to analyze large-scale datasets, ensuring robust results.
• Creates efficient data pipelines and digital frameworks for electronic health data management.
• Ensures compliance with data privacy and security standards.
• Coordinates and oversees data collection processes using various methodologies.
• Prototypes, troubleshoots, and fine-tunes machine learning models for outcome predictions relevant to surgery.
• Designs, trains, and evaluates neural networks for computer vision tasks related to surgery.
• Develops and maintains software packages for prototype systems used in surgical outcome analysis.
• Communicates research findings effectively through presentations and reports.
• Assists the PI in preparing grant proposals and contributes to research plans.
• Leads or contributes to writing scientific manuscripts for peer-reviewed journals.

Requirements

• Ph.D. in Chemistry, Computational Sciences, Computational Biology, Structural Biology, Computer Science, Bioinformatics, Statistics, or related disciplines.
• Proficient programming skills, particularly in Python.
• Experience in large-scale machine learning modeling.

Nice-to-haves

• Proficiency in machine learning and statistical analysis.
• Experience with tools such as R, TensorFlow, and PyTorch.
• Strong background in analyzing large-scale healthcare datasets.
• Familiarity with electronic health records.