Postdoctoral Fellow, TEAM-AI Lab, Department of Quantitative and Systems Health Sciences

Job Posting Title: Postdoctoral Fellow, TEAM-AI Lab, Department of Quantitative and Systems Health Sciences Job Details Hiring Department: Quantitative and Systems Health Science (QSHS) Position Open To: All Applicants Weekly Scheduled Hours: 40 FLSA Status: Exempt from FLSA Earliest Start Date: Immediately Position Duration: Expected to Continue Until Aug 31, 2027 Location: AUSTIN, TX Purpose The TEAM-AI Lab seeks multiple Postdoctoral Research Associates to lead methodological innovation, software architecture engineering, and scientific execution across its active grant portfolio. Working under the direct mentorship of Dr. Hongfang Liu and lab faculty, the Postdoctoral Researcher will drive research at the intersection of health data science, multimodal AI, digital twins, computational phenotyping, and responsible AI. PhD must have been received within the last three years. The candidate will hold primary responsibility for designing novel algorithmic frameworks, coordinating multi-institutional research networks, and translating real-world health data into actionable clinical intelligence. This position provides structured preparation for an academic tenure-track career or lead research scientist position in industrial AI labs, providing access to national data networks, high-performance computing clusters, and clinical interdisciplinary collaborations across UT Austin. The applicants will join a collaborative research environment at the Translational AI Excellence and Application in Medicine (TEAM-AI) Lab, focusing on accelerating the translation of AI innovations in biomedicine and healthcare. The lab consists of faculty members, program managers/coordinators, data scientists, and scientific programmers. The activities carried out by the team range from advancing AI innovations through big data, empowering biomedical and clinical sciences through team science collaboration and best practices, to building human-centered, value-added, and evidence-based tools, resources, and services to facilitate real-world implementation of said innovations. This position is a temporary with an end date of 08/31/27, renewable based upon availability of funding, work performance, and progress toward goals. Grant reference EMED: An Ethical Mixture-of-Experts Digital Twin Framework for Medical Device Surveillance CardioOnco-AI: AI-Empowered Cardiotoxicity Risk Prediction Among Breast Cancer Survivors Using Multi-Site Real-World Data: ReCARDO: Using Real-World Data to Derive Common Data Elements for Alzheimer's Disease and AD-Related Dementias Research Through Ontological Innovation: WONDER: Accelerating Real World Data-driven Precision Oncology through Data Science and Informatics Excellence in Research: POI-KB: Design and Development of a Knowledgebase for Accelerating Perioperative Organ Injury Research and Translation Responsibilities Lead the design and implementation of mixture-of-experts neural architectures and reinforcement learning pipelines for counterfactual disease trajectory simulation for EMED, an NIH-funded multi-modal AI project. Architect and evaluate multi-site cardiotoxicity risk prediction models integrating structured EHRs, clinical notes via natural language processing, strain echocardiography features, and non-medical determinants of health under the FDA CardioOnco-AI award. Coordinate AI and computational phenotyping work streams within the national 10-institution ReCARDO network to extract, standardize, and validate Common Data Elements (CDEs) for Alzheimer's disease research. Construct deep language models and clinical natural language processing pipelines to extract structured oncologic phenotypes, molecular biomarkers, and treatment responses from progress notes for the WONDER project. Engineer semantic knowledge graphs and database query architectures capturing perioperative pathophysiological mechanisms for acute organ injury research under POI-KB. Authorship of high-impact first-author or co-author manuscripts in leading informatics and machine learning journals and conferences. Other related duties as assigned. Education & Experience Minimum Qualifications Ph.D. in Biomedical Informatics, Computer Science, Data Science, Electrical & Computer Engineering, Applied Mathematics, or a related quantitative field earned within the past three years. Strong background in one or more of the following areas: generative and trajectory modeling including transformers, mixture-of-experts, reinforcement learning, simulation, and counterfactual analysis. Multimodal NLP & Fusion, large language models (LLMs), cross-attention Fusion, vision-language transformers. Ontological engineering, knowledge graph construction and mining, CDE development for data harmonization. Regulatory science and explainable AI, verification, validation, uncertainty quantification, and AI evaluation framework High-performance computing and big data analytics PhD must have been received within the last three years. Preferred Qualifications Demonstrated understanding of model validation, clinical trial design, and causal inference techniques. Experience with transformer-based models, LLMs, retrieval-augmented generation (RAG), or foundation models. Experience analyzing complex real-world clinical datasets (e.g., MIMIC-IV, OMOP CDM, PCORnet, NACC Uniform Data Set, or state cancer registries). Knowledge of causal inference, clinical prediction modeling, or multimodal AI. Experience with responsible AI, model evaluation, fairness, privacy, or explainable AI. Experience with biomedical image processing, radiomics, or digital pathology. Scientific programming on Linux or high-performance computing environments.

LICENSES, REGISTRATIONS OR CERTIFICATIONS

Required None Preferred None Salary Range $63,480+ depending on NIH Level

WORKING ENVIRONMENT/EQUIPMENT

Standard office equipment. Repetitive use of a keyboard. May be exposed to such occupational hazards as communicable diseases, blood borne pathogens, ionizing and non-ionizing radiation, hazardous medications and disoriented or combative patients, or others. May work in research laboratories, clinical environments, hospitals, ambulatory settings, or field research locations. May handle biological specimens, chemicals, hazardous materials, or laboratory equipment consistent with assigned research activities. May periodically lift and move research materials and equipment in accordance with organizational safety requirements. Requires visual acuity and manual dexterity sufficient to operate research equipment and computer systems. May require occasional evening, weekend, or travel commitments for research activities, conferences, and collaborative projects. Required Materials Resume/CV 3 work references with their contact information; at least one reference should be from a supervisor Letter of interest Employment Eligibility Please make sure you meet all the required qualifications and you can perform all the essential functions with or without a reasonable accommodation. Retirement Plan Eligibility 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. Background Checks A criminal history background check will be required for finalist(s) under consideration for this position. Equal Opportunity Employer 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. Pay Transparency 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 disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by the employer, or (c) consistent with the contractor’s legal duty to furnish information. Employment Eligibility Verification If hired, you will be required to complete the federal Employment Eligibility Verification I-9 form. You will be required to present acceptable and original documents to prove your identity and authorization to work in the United States. Documents need to be presented no later than the third day of employment. Failure to do so will result in loss of employment at the university. E-Verify The University of Texas at Austin use E-Verify to check the work authorization of all new hires effective May 2015. The university’s company ID number for purposes of E-Verify is 854197. For more information about E-Verify, please see the following: E-Verify Poster (English and Spanish) [PDF] Right to Work Poster (English) [PDF] Right to Work Poster (Spanish) [PDF] Compliance Employees may be required to report violations of law under Title IX and the Jeanne Clery Disclosure of Campus Security Policy and Crime Statistics Act (Clery Act). If this position is identified a Campus Security Authority (Clery Act), you will be notified and provided resources for reporting. Responsible employees under Title IX are defined and outlined in HOP-3031. The Clery Act requires all prospective employees be notified of the availability of the Annual Security and Fire Safety report. You may access the most recent report here or obtain a copy at University Compliance Services, 1616 Guadalupe Street, UTA 2.206, Austin, Texas 78701. #J-18808-Ljbffr The University of Texas at Austin