Postdoctoral Fellow, AI Driven Precision Oncology
About The Position
The Kowalski Lab at the University of Texas at Austin invites applications for a Postdoctoral Fellow position focused on developing advanced, AI-enabled methods for clinical decision support in precision oncology. The fellow will work at the intersection of computational innovation, translational science, and patient-centered care, contributing to pioneering efforts in integrating multi-modal data for individualized cancer therapy selection. The lab leads multi-institutional projects combining clinical, molecular, proteomic, and other published data to build explainable and scalable decision-support systems. These systems are designed to bridge gaps in personalized treatment for patients with rare, resistant, or genomically un-targetable cancers.
Requirements
- PhD in computational biology, bioinformatics, computer science, information science, biomedical engineering, or a related field.
- PhD must have been received within the last three years, 1 year of experience with machine learning, natural language processing, AI tools and frameworks, data integration, and/or explainable AI.
- Proficiency in Python and R for use in data science and modeling.
- Excellent writing and communication skills; demonstrated publication record.
Nice To Haves
- Knowledge of cancer biology, clinical oncology workflows, or multi-omics data.
Responsibilities
- Design and evaluate algorithms for treatment and response matching using integrated clinical and molecular datasets.
- Develop knowledge graphs and multimodal embeddings for cancer patient digital twin construction.
- Lead and co-author high-impact publications and grant proposals.
- Collaborate with clinicians, bioinformaticians, and data scientists across UT Austin, and other partners.
- Mentor graduate and undergraduate research assistants and contribute to lab leadership.
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What This Job Offers
Job Type
Full-time
Education Level
Ph.D. or professional degree
Number of Employees
5,001-10,000 employees
