Postdoctoral Fellow, AI Driven Precision Oncology

Postdoctoral Fellow, AI Driven Precision Oncology

Hiring Department: Department of Medicine

Position Open To: All Applicants

Weekly Scheduled

Hours:

40

FLSA Status: Exempt

Earliest

Start Date:

Immediately

Position Duration: Expected to Continue Until Aug 31, 2029

Location: UT MAIN CAMPUS

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.

• 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.

• 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.

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.

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

$62,232+ depending on NIH level

• Standard office equipment
• Repetitive use of a keyboard

Please make sure you meet all the required qualifications and you can perform all of the essential functions with or without a reasonable accommodation.

The retirement plan for this position is Teacher Retirement System of Texas (TRS), subject to the position being at least 20 hours per week and at least 135 days in length. This position has the option to elect the Optional Retirement Program (ORP) instead of TRS, subject to the position being 40 hours per week and at least 135 days in length.

A criminal history background check will be required for finalist(s) under consideration for this position.

The University of Texas at Austin, as an equal opportunity/affirmative action employer, complies with all applicable federal and state laws regarding nondiscrimination and affirmative action. The University is committed to a policy of equal opportunity for all persons and does not discriminate on the basis of race, color, national origin, age, marital status, sex, sexual orientation, gender identity, gender expression, disability, religion, or veteran status in employment, educational programs and activities, and admissions.

The University of Texas at Austin will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. However, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot…