Research Assistant Professor - Machine Learning and AI Brain Mapping

Overview

Location:

El Paso, TX | Category:
Science | Job Type: Full-time | Posted On:
Wed May 27 2026

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.

• 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 Brain Globe 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.

• 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…