Research Assistant Professor Machine Learning and AI for Brain Mapping

The University of Texas at El Paso (UTEP) is a Carnegie R1 equal opportunity and community engaged university at the heart of the U.S.-Mexico border region that is increasing access to excellent higher education. We advance discovery of public value and positively impact the health culture education and economy of the community we serve. UTEP enrolls more than 25000 students 85% of whom are Hispanic and half of whom are the first in their families to attend college. UTEP offers 171 bachelors masters and doctoral degree programs at the only open-access top-tier research university in America.

Located at the westernmost tip of Texas El Paso is set against the backdrop of the Franklin Mountains and the Chihuahuan Desert in one of the largest binational communities in the world. A family-oriented community that is one of the safest large cities in the U.S. the cost of living in El Paso is very reasonable with an average home value of $224000 in 2025 and Texas has no income tax. The regions rich culture includes a vibrant street art and mural scene art galleries a new childrens museum and science center the states longest-running symphony orchestra and a full schedule of seasonal events and festivals. Sports fans will enjoy Division I college athletics Triple-A baseball affiliate the El Paso Chihuahuas and professional soccer team El Paso Locomotive FC and outdoor enthusiasts will be drawn to year-round hiking mountain biking and rock climbing in nearby state and national parks.

About the Department of Biological Sciences

The Department of Biological Sciences offers two Ph.D. tracks (Bioscience and Ecology and Evolutionary Biology) and has over 100 graduate students and over 2000 undergraduate majors including over 150 neuroscience majors. Biological sciences faculty maintain strong collaborative and programmatic ties with several other departments including Psychology Pharmacy Physics Chemistry and Computer Science. Computer Science is a particularly close partner: it houses UTEPs Master of Science in Artificial Intelligence (M.S. in AI) program and includes faculty whose research intersects with neuroscience biomedical imaging and computational data analysis.

The department hosts a vibrant neuroscience research community with an active program in mesoscale chemoarchitectural mapping of the rat brain spanning experimental neuroanatomy atlas-based data integration and computational tools that preserve the spatial provenance of molecular and physiological datasets.

The cluster hire described in this announcement extends these strengths toward an integrated research program on the neural circuitry of craving reward and addiction. This work is anchored by an NIH-funded Imaging & Behavioral Neuroscience Core Facility which provides three-dimensional microscopic imaging of the brain at the microcircuit level and is supported by multiple active federal awards in neuroscientific research. The department also sits within a broader collaborative research environment with access to core facilities in imaging computing and data science.

The Department of Biological Sciences at The University of Texas at El Paso (UTEP) is hiring a non-tenure-track Research Assistant Professor in Machine Learning and AI for Brain Mapping. The position is one of four in a coordinated cluster hire -alongside colleagues in behavioral neuroscience brain circuit imaging and atlas mapping and research software engineering -studying the brain circuits underlying craving reward and addiction. The hire will develop ML/AI methods for an integrated rat-to-atlas brain mapping pipeline drawing on light-sheet 3-D microscopy from UTEPs NIH-funded Imaging & Behavioral Neuroscience Core Facility and on expert-curated maps from Brain Maps 4.0 and Chemopleth 1.0 as ground-truth training data. Two methodological pillars anchor the role: computer-vision pipelines for image registration and segmentation that bring raw 3-D imaging into the atlas framework and spatial AI that supports cross-layer queries of co-registered atlas data. The role pairs naturally with UTEPs Institute for Applied AI Innovation (AAII) and the Master of Science in Artificial Intelligence (M.S. in AI) program where the hire can mentor graduate researchers draw practicum-style projects from the clusters atlas work and add an AI dimension to the Brain Mapping & Connectomics (BM&C) undergraduate teaching laboratorys curriculum. As deep-learning methods proliferate in neuroanatomy this role sets the standard for scientific rigor -building models that respect spatial provenance anatomical interpretability and reproducibility against expert-curated ground truth rather than chasing benchmark metrics disconnected from biological meaning.

Position Responsibilities

• Lead the adoption of rigorous scientific ML/AI practices for the clusters modeling and analysis work including reproducible experiment tracking principled model evaluation validation against expert-curated ground truth and transparent reporting of methods data and results.
• Design develop and evaluate machine learning and AI methods for the rat-to-atlas mapping pipeline including ML-based image registration segmentation and feature extraction applied to light-sheet 3-D microscopy data and automated atlas-based annotation.
• Build cross-layer query capabilities over the clusters stack of atlas-registered data enabling integrated interrogation of any mapped region across multiple data modalities (gene expression connectivity physiology behavioral activation and others) as a core value-add of the digital atlas environment.
• Develop ML/AI methods grounded in the Brain Maps 4.0 and Chemopleth 1.0 frameworks using expert-curated maps as ground-truth training and validation data with attention to interoperability with international neuroinformatics infrastructure such as EBRAINS and the BrainGlobe ecosystem.
• Collaborate with cluster-hire colleagues in behavioral neuroscience circuit imaging and mapping and research software engineering to translate scientific questions into ML/AI approaches and to support multi-modal data integration and atlas development.
• Contribute to peer-reviewed publications and federal grant applications describing the clusters ML/AI methods datasets and modeling outputs including the open-access digital atlas of brain reward circuits.
• Contribute to the Brain Mapping & Connectomics (BM&C) undergraduate teaching laboratory by introducing AI-based mapping methods into its curriculum and mentoring students as contributors to the clusters research pipeline.
• Engage with UTEPs Institute for Applied AI Innovation (AAII) and the M.S. in Artificial Intelligence program through mentorship of graduate researchers supervision of student capstone or thesis projects drawn from the clusters atlas work and participation in programmatic activities.

• Ph.D. in computer science machine learning biomedical engineering computational neuroscience applied mathematics computational or mathematical sciences or a related field; or a Masters degree with substantial professional ML/AI research experience.
• Experience with rigorous ML/AI research practices including reproducible experiment tracking principled model evaluation validation against ground truth and transparent reporting.
• Demonstrated research experience in computer vision and deep learning applied to biological microscopy data -including image registration segmentation and feature extraction with architectures such as U-Net and its 3-D variants in modern ML frameworks (e.g. PyTorch) -evidenced by peer-reviewed publications preprints or open-source contributions.
• Demonstrated experience with spatial-AI or related spatial-data methods (e.g. multi-layer spatial queries spatial statistical modeling or atlas-based registration of multi-modal data).
• Experience with the technical infrastructure for scalable biomedical imaging research including handling large volumetric microscopy datasets (e.g. light-sheet or serial-section reconstructions of whole rodent brains; memory-efficient I/O tile-based or chunked processing multi-resolution formats such as OME-Zarr) and GPU computing on cloud or HPC environments.
• Experience mentoring or training undergraduate or graduate students in ML/AI methodology including supervision of student modeling projects.
• Demonstrated interest in or experience with neuroscience biomedical imaging or related scientific domains.
• Demonstrated ability to work in interdisciplinary teams that connect ML/AI methodology to scientific questions and experimental data.

Preferred Qualifications

• Experience working in academic or research-intensive environments including open-source ML or scientific projects.
• Familiarity with neuroscience or biomedical data formats and standards (e.g. NIfTI BIDS OME-TIFF).
• Foundation in classical computer vision and image-processing methods complementing modern deep-learning approaches.
• Familiarity with the deep-learning and analysis tool ecosystem used in mesoscale rodent brain mapping -spanning cellular/sub-cellular segmentation (e.g. Cellpose StarDist) whole-brain mesoscale pipelines (e.g. cellfinder brainreg brainmapper DeepSlice) and image inspection platforms (e.g. Napari Fiji/ImageJ QuPath).
• Experience with model interpretability methods and validation against expert-curated ground truth annotations.
• Engagement with shared neuroscience infrastructure and atlas frameworks including FAIR/open-science practices the BrainGlobe ecosystem EBRAINS NIH BRAIN Initiative resources and Brain Maps 4.0 or analogous mesoscale chemoarchitectural atlases.
• Additional familiarity with graph theory and connectomic analyses network neuroscience or NLP-based biomedical literature mining -complementary methodologies that may support collaboration with external network-neuroscience and informatics partners.
• Track record of independent grant submissions or co-authored funded proposals related to ML/AI computational neuroscience or scientific computing.
• Experience teaching mentoring or supervising graduate research within an AI research institute or masters-level AI program.

Appointment: Non-tenure-track Research Assistant Professor. Initial appointment is for 12 months renewable contingent on performance and funding availability. The position can be renewed for a maximum of 3 years; renewal beyond 3 years will depend on the candidates ability to secure extramural funding.

Salary: Commensurate with experience and qualifications. The salary will depend on the candidates qualifications and experience and includes excellent fringe benefits. Hiring decisions are based on budget approval.

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 employees or prospective employees 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 by email at 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 UTEPs Equal Opportunity Office at .

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.

A complete application includes:

• Cover letter describing research interests relevant experience and fit with the position
• Curriculum vitae
• Statement of research (maximum of 2 pages)
• Names and contact information for three professional references
• Up to three representative publications preprints or software/data artifacts

Review of applications will begin immediately and continue until the position is filled. Questions about the position may be directed to the search committee chair Dr. Arshad M. Khan ().