"Associate or Full Professor, Host-Microbe Interactions, Data Analytics and Public Health Cluster"

Job Description

The University of Texas at San Antonio, College of Sciences, College of AI Cyber and Computing, and the College of Health Community and Policy and the Departments of Molecular Microbiology and Immunology, Computer Engineering and Public Health, invites applications for the position of Associate Professor or Professor, to be appointed as a University of Texas System (UT System) Research Excellence Regents' Professor. Successful candidates will be part of a strategic cluster hiring initiative focused on advancing research and fostering transdisciplinary integration using Artificial Intelligence, Deep Learning, and Big Data Analytics to address complex challenges in infectious diseases, human immunology, and public health, with an anticipated start date in the Fall of the 2026-27 academic year.

The University of Texas System approved the creation of the Regents' Research Excellence Program across its four Emerging Research Universities (ERUs), including UT San Antonio. UT System has allocated funds across all four ERUs to fund the recruitment of research-active faculty to dramatically grow its national research prominence and federal funding opportunities. The university's allocation from UT System translates to approximately 40 new faculty positions for new, mid- to senior-level faculty over the next several years who will add expertise in research areas that will enhance competitiveness, help solve societal needs, and advance the university's capacity to meet UT System and state goals as outlined by the Texas Legislature.

UT San Antonio is utilizing our Clustered & Connected Hiring Program (CCP), which is designed to recruit and hire some of the best and brightest minds of varying backgrounds and experiences in select fields to UT San Antonio to join in efforts to address some of today's most significant challenges.

For the Host-Microbe Interactions, Data Analytics and Public Health CCP, three positions are open in the following areas:

• Active Immunization Design & Optimization, Real-Time Pathogen Detection & Diagnosis: Candidates are sought who have expertise in cellular and molecular approaches of host-microbe interactions and/or applications of AI-powered diagnostics, deep learning for imaging and molecular data, real-time genomic analysis for pathogen detection and antimicrobial resistance, and the application of data science and statistics to accelerate vaccine development and integrate host-pathogen genomic data among other public health applications
• Artificial Intelligence and Deep Learning in Microbiome and Other Microbial Ecosystem Analysis: Candidates are sought who apply Artificial Intelligence and Deep Learning to analyze complex microbial communities across expansive environments, including the human body, or that support the processing of large-scale datasets to identify microbial and host cell compositions and/or interactions, and their roles in health and disease.
• Predictive Modeling for Infectious Diseases Outbreak and Population Health: Candidates are sought who integrate molecular, cell biology or biochemistry with Artificial Intelligence and Deep Learning in combination with epidemiological data, data science, and statistics to support predictive modeling of infectious disease outbreaks. By analyzing datasets such as population demographics, travel patterns, environmental factors, host responses to single and polymicrobial infections, and pathogen characteristics. These models can forecast disease spread, evaluate intervention strategies, and guide resource allocation for effective prevention and control.

Application Information

We seek outstanding candidates who have independent, cutting-edge research programs in infection and immunity, or host-microbe interactions, with emphasis on

• Active Immunization Design & Optimization, Real-Time Pathogen Detection & Diagnosis
• Artificial Intelligence and Deep Learning in Microbiome and Other Microbial Ecosystem Analysis
• Predictive Modeling for Infectious Diseases Outbreak and Population Health

Each candidate is expected to have an extramurally funded research program and to participate in teaching at the undergraduate and/or graduate level. Successful candidates are expected to support the University's mission as an R1 academic enterprise and its strong commitment to student success.

The successful candidate will join a highly interactive and collaborative group of faculty across the departments of Molecular Microbiology and Immunology, Computer Engineering or Public Health. Faculty at the South Texas Center for Emerging Infectious Diseases (STCEID), drive advances in microbiology, immunology, host-pathogen interactions, vaccine development, and drug discovery, supported by cutting-edge facilities in genomics, proteomics, advanced imaging, and BSL3 laboratories. These efforts are amplified by MATRIX AI Consortium for Human Well-Being, where experts in artificial intelligence, data science, and machine learning collaborate with biomedical and public health researchers to harness big data, model infectious disease dynamics, and develop innovative interventions. Together, these strengths create a uniquely transdisciplinary environment where discovery, computation, data science and application converge to improve health and strengthen communities.

Required Qualifications

• Ph.D. or equivalent degree and have an established national reputation in their field.
• Have a strong record of collaborative research partnerships bridging infection and immunity, or host-microbe interactions with AI, Machine Learning and Computing Engineering, who can foster collaborations across departments and external partners, while demonstrating the ability to develop and sustain an independent research program.
• Applicants must have an externally funded research program and an outstanding record of scholarly productivity.
• Expertise in interdisciplinary areas of microbiology and immunology, antimicrobial resistance, chronic diseases, community health, data-centric analyses and applications ‘Omics' integrations with human immune responses and disease outcomes, health informatics, statistics and data science will also be considered.
• A commitment to contributing to the university's instructional mission, including teaching and mentoring undergraduate and graduate students is essential.
• We seek scholars who embrace UT San Antonio's values of collegiality, academic excellence, and transdisciplinary collaboration.
• This position requires the ability to maintain the security and integrity of UT San Antonio and its infrastructure.

To apply, applicants must upload the following:

• A current curriculum vitae.
• Complete contact information for at least three professional references.
• A research statement and summary of research goals (3-page limit).
• A teaching statement and teaching philosophy (1 page limit).
• A statement highlighting potential areas for transdisciplinary collaboration (1-page limit).

Tenure is contingent upon Board of Regents approval, and applicants must show a demonstrated commitment. This position requires the ability to maintain the security and integrity of UT San Antonio and its infrastructure.

All materials must be submitted at the UT San Antonio Human Resources website: https://www.utsa.edu/jobs/. This position will remain open until filled. To ensure full consideration, please apply by 12/31/2025. Incomplete applications will not be accepted. For more information, contact Dr. Astrid Cardona, Chair of the Search Committee at astrid.cardona@utsa.edu.

Salary is competitive and commensurate with qualifications and experience. UT San Antonio offers an attractive package of benefits, including medical, dental, and vision coverage plus participation in the Teacher Retirement System or an Optional Retirement System. More information can be found at www.utsa.edu/benefits/.

Cluster Hiring Interview Process

UT San Antonio aims to bring transdisciplinary and collaborative groups of faculty researchers to the university through its CCP program. As such, the on-campus interview for these positions will be conducted as a collaborative group interview, during which all on-campus candidates for a position will meet with university faculty and staff simultaneously. This collaborative process allows candidates to discuss potential research collaborations with fellow candidates and current faculty. Additionally, each candidate will be given the chance to present their job talk and engage the faculty, staff, and students from their discipline's home department.

This position will work primarily on campus. Travel and parking expenses are the employee's responsibility.

This position is contingent upon a successful background check. Applicants selected must be able to show proof of eligibility to work in the United States by time of hire.