The University of Texas at El Paso
Research Assistant Professor in Computational Biology with AI/ML
The University of Texas at El Paso, El Paso, Texas, us, 88568
Research Assistant Professor in Computational Biology with AI/ML
Location:
El Paso, TX
Category:
Science
Posted On:
Thu Dec 4 2025
The Department of Biological Sciences at the University of Texas at El Paso (UTEP) invites applications for a non‑tenure‑track faculty position as a Research Assistant Professor in the areas of Computational Biology with AI/ML, beginning Spring 2026. The appointments will be for 12 months, and the position can be renewed for a maximum of three years; renewal beyond three years will depend on the successful candidate’s ability to secure extramural funding. We seek an innovative, highly collaborative scientist with a strong background in Computational Biology/Genetics/Genomics/Bioinformatics/Molecular Biology. Candidates with expertise in applying AI/ML to high‑throughput omics data, processing, and curating large‑scale omics datasets, are especially encouraged to apply.
The successful candidate will work with a team of multi‑omics researchers within the Department of Biological Sciences. They are expected to participate in collaborative research programs across the colleges of Science, Health Sciences, Engineering, and the School of Pharmacy; secure extramural funding independently or collaboratively; publish in high‑impact peer‑reviewed journals; contribute to the training of undergraduate and graduate students, postdoctoral fellows, and technical staff. The candidate will also conduct workshops and training sessions in the use of AI/ML related to omics data analysis.
Job Requirements
Grant writing & extramural funding. Experience writing or contributing to instrumentation grants, research grants, or collaborative proposals.
Demonstrated success in collaborative research and peer‑reviewed publications.
Proficiency in Python and R for data analysis, modeling, and visualization.
Proficiency in building efficient pipelines that use optimized software to process large datasets.
Proficiency in supervised, unsupervised, and deep learning.
Proficiency in appropriate model selection; simpler or complex models, depending on the data.
Ability to work effectively within diverse, multidisciplinary teams of faculty, staff, and students from varied ethnic, cultural, and socioeconomic backgrounds.
Commitment to involving students in research, mentoring, and publication opportunities.
Compliance, safety, and regulatory knowledge.
Knowledge of laboratory safety, chemical hygiene, and data integrity standards (e.g., QA/QC practices).
Preferred Skill Sets for Competitive Candidates Include
Experience in analyzing genomics, transcriptomics, and proteomics data sets.
Application of advanced statistical modeling and bioinformatics tools for comprehensive multi‑omics data integration and interpretation.
Proficiency in hybrid data analysis.
Proficiency in integrating diverse datasets, such as phenotypic, genotypic, and imaging data, to build more robust and insightful models.
Successful teaching experience at the university or community college level.
Strong communication and reporting skills.
Core users and collaborators depend on reporting and documentation.
Ability to generate clear reports, SOPs, training materials, workflows, and user documentation.
Data management & computational reproducibility.
Experience with data pipelines, version control (e.g., GitHub), reproducible workflows, and cloud or HPC environments.
Additional Information In keeping with its access, excellence, and impact mission, the University of Texas at El Paso is committed to an open, diverse, and inclusive learning and working environment that honors the talents, respects the differences, and nurtures the growth and development of all. We seek to attract faculty and staff who share our commitment.
The University of Texas at El Paso is an Equal Opportunity Employer. The University does not discriminate on the basis of race, color, national origin, sex, religion, age, disability, genetic information, veteran status, or sexual orientation and gender in employment or the provision of services in accordance with state and federal law. Discrimination on the basis of sex includes an employee's or prospective employee's right to be free from sexual harassment under Title IX of the Higher Education Amendments of 1972. Inquiries—including the filing of a Formal Complaint or reporting an incident—about the application of Title IX may be referred to the Title IX Coordinator, who can be reached by phone at (915) 747‑8358, by email at titleix@utep.edu, or by mail at 500 W. University Ave., El Paso, TX, Kelly Hall, Room 312.
For accommodation information for employees and applicants with disabilities, please contact UTEP's Equal Opportunity Office at eoaa@utep.edu.
To the extent that this position involves research, work, or access to critical infrastructure as referenced in Executive Order GA‑48, being hired for and continuing to be employed in this position requires the ability to maintain the security or integrity of the infrastructure.
Seniority level
Entry level
Employment type
Full‑time
Job function
Education and Training
Industries: Higher Education
#J-18808-Ljbffr
El Paso, TX
Category:
Science
Posted On:
Thu Dec 4 2025
The Department of Biological Sciences at the University of Texas at El Paso (UTEP) invites applications for a non‑tenure‑track faculty position as a Research Assistant Professor in the areas of Computational Biology with AI/ML, beginning Spring 2026. The appointments will be for 12 months, and the position can be renewed for a maximum of three years; renewal beyond three years will depend on the successful candidate’s ability to secure extramural funding. We seek an innovative, highly collaborative scientist with a strong background in Computational Biology/Genetics/Genomics/Bioinformatics/Molecular Biology. Candidates with expertise in applying AI/ML to high‑throughput omics data, processing, and curating large‑scale omics datasets, are especially encouraged to apply.
The successful candidate will work with a team of multi‑omics researchers within the Department of Biological Sciences. They are expected to participate in collaborative research programs across the colleges of Science, Health Sciences, Engineering, and the School of Pharmacy; secure extramural funding independently or collaboratively; publish in high‑impact peer‑reviewed journals; contribute to the training of undergraduate and graduate students, postdoctoral fellows, and technical staff. The candidate will also conduct workshops and training sessions in the use of AI/ML related to omics data analysis.
Job Requirements
Grant writing & extramural funding. Experience writing or contributing to instrumentation grants, research grants, or collaborative proposals.
Demonstrated success in collaborative research and peer‑reviewed publications.
Proficiency in Python and R for data analysis, modeling, and visualization.
Proficiency in building efficient pipelines that use optimized software to process large datasets.
Proficiency in supervised, unsupervised, and deep learning.
Proficiency in appropriate model selection; simpler or complex models, depending on the data.
Ability to work effectively within diverse, multidisciplinary teams of faculty, staff, and students from varied ethnic, cultural, and socioeconomic backgrounds.
Commitment to involving students in research, mentoring, and publication opportunities.
Compliance, safety, and regulatory knowledge.
Knowledge of laboratory safety, chemical hygiene, and data integrity standards (e.g., QA/QC practices).
Preferred Skill Sets for Competitive Candidates Include
Experience in analyzing genomics, transcriptomics, and proteomics data sets.
Application of advanced statistical modeling and bioinformatics tools for comprehensive multi‑omics data integration and interpretation.
Proficiency in hybrid data analysis.
Proficiency in integrating diverse datasets, such as phenotypic, genotypic, and imaging data, to build more robust and insightful models.
Successful teaching experience at the university or community college level.
Strong communication and reporting skills.
Core users and collaborators depend on reporting and documentation.
Ability to generate clear reports, SOPs, training materials, workflows, and user documentation.
Data management & computational reproducibility.
Experience with data pipelines, version control (e.g., GitHub), reproducible workflows, and cloud or HPC environments.
Additional Information In keeping with its access, excellence, and impact mission, the University of Texas at El Paso is committed to an open, diverse, and inclusive learning and working environment that honors the talents, respects the differences, and nurtures the growth and development of all. We seek to attract faculty and staff who share our commitment.
The University of Texas at El Paso is an Equal Opportunity Employer. The University does not discriminate on the basis of race, color, national origin, sex, religion, age, disability, genetic information, veteran status, or sexual orientation and gender in employment or the provision of services in accordance with state and federal law. Discrimination on the basis of sex includes an employee's or prospective employee's right to be free from sexual harassment under Title IX of the Higher Education Amendments of 1972. Inquiries—including the filing of a Formal Complaint or reporting an incident—about the application of Title IX may be referred to the Title IX Coordinator, who can be reached by phone at (915) 747‑8358, by email at titleix@utep.edu, or by mail at 500 W. University Ave., El Paso, TX, Kelly Hall, Room 312.
For accommodation information for employees and applicants with disabilities, please contact UTEP's Equal Opportunity Office at eoaa@utep.edu.
To the extent that this position involves research, work, or access to critical infrastructure as referenced in Executive Order GA‑48, being hired for and continuing to be employed in this position requires the ability to maintain the security or integrity of the infrastructure.
Seniority level
Entry level
Employment type
Full‑time
Job function
Education and Training
Industries: Higher Education
#J-18808-Ljbffr