General Notes

This is a grant funded position with an end date 1 year from the start date. The position is renewable based upon availability of funding, work performance, and progress toward goals with the option to continue until August 31, 2029, if renewed. Note: This candidate must be authorized to work in the United Stated without sponsorship.

Purpose

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.

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.

Learning Opportunities:

Develop and deploy innovative AI models for treatment discovery and patient-specific decision support.
Gain experience in translational research across clinical, academic, and technology domains.
Participate in lab initiatives aligned with NCI, CPRIT, and NIH-funded projects.

Required Qualifications

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.

Preferred Qualifications

Knowledge of cancer biology, clinical oncology workflows, or multi-omics data.

Salary Range

$62,232+ depending on NIH level

Working Conditions

Standard office equipment
Repetitive use of a keyboard

Required Materials

Resume/CV
3 work references with their contact information; at least one reference should be from a supervisor
Letter of interest

Important for applicants who are NOT current university employees or contingent workers: You will be prompted to submit your resume the first time you apply, then you will be provided an option to upload a new Resume for subsequent applications. Any additional Required Materials (letter of interest, references, etc.) will be uploaded in the Application Questions section; you will be able to multi-select additional files. Before submitting your online job application, ensure that ALL Required Materials have been uploaded. Once your job application has been submitted, you cannot make changes.

Important for Current university employees and contingent workers: As a current university employee or contingent worker, you MUST apply within Workday by searching for Find UT Jobs. If you are a current University employee, log-in to Workday, navigate to your Worker Profile, click the Career link in the left hand navigation menu and then update the sections in your Professional Profile before you apply. This information will be pulled in to your application. The application is one page and you will be prompted to upload your resume. In addition, you must respond to the application questions presented to upload any additional Required Materials (letter of interest, references, etc.) that were noted above.

Dell Medical School

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