Postdoctoral Fellow, AI Driven Precision Oncology

Job Posting Title

Postdoctoral Fellow, AI Driven Precision Oncology

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

This is a grant funded position with a one year end date from the start date. The role is renewable based on funding, performance, and progress toward goals, with the option to continue until August 31, 2029.

Candidate must be authorized to work in the United States without sponsorship.

The Kowalski Lab at the University of Texas at Austin invites applications for a Postdoctoral Fellow focused on developing advanced, AI-enabled methods for clinical decision support in precision oncology.

The fellow will operate 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 that bridge gaps in personalized treatment for patients with rare, resistant, or genomically un‑targetable cancers.

• Design and evaluate algorithms for treatment and response pairing 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. One 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 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

• Resume/CV
• Three work references with contact information (at least one from a supervisor)
• Letter of interest

For non‑current university employees or contingent workers:
Submit your resume first time you apply, then upload additional materials (letter of interest, references, etc.) in the Application Questions section. For current university employees or contingent workers:
Apply within Workday, upload resume, and respond to application questions to provide additional required materials.

Please ensure you meet all required qualifications and can perform all essential functions, with or without reasonable accommodation.

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.