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Research Assistant Professor - Machine Learning and AI for Brain MappingUniversity of Texas at El Paso • United States
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Research Assistant Professor - Machine Learning and AI for Brain Mapping

University of Texas at El Paso
  • US
    United States
  • US
    United States
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Über

Job Description:
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 UTEP's 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 UTEP's 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 cluster's atlas work, and add an AI dimension to the Brain Mapping & Connectomics (BM&C) undergraduate teaching laboratory's 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 cluster's 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 cluster's 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 cluster's 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 cluster's research pipeline. Engage with UTEP's 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 cluster's atlas work, and participation in programmatic activities. Requirements:
Ph.D. in computer science, machine learning, biomedical engineering, computational neuroscience, applied mathematics, computational or mathematical sciences, or a related field; or a Master's 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 master's-level AI program. Additional Information:
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 candidate's ability to secure extramural funding.
Salary: Commensurate with experience and qualifications. The salary will depend on the candidate's 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 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.
Application Instructions:
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 (amkhan2@utep.edu).
  • United States

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  • English
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