Job Description

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 Software Engineering. The position is one of four in a coordinated cluster hire - joining colleagues in behavioral neuroscience, brain circuit imaging and atlas mapping, and machine learning and AI - that will expand a cross-college team studying the brain circuits underlying craving, reward, and addiction. The hire will lead software development for an integrated rat-to-atlas brain mapping pipeline and its atlas infrastructure: the open-access atlas Brain Maps 4.0 and the recently released Chemopleth 1.0 spatial database. The role pairs naturally with UTEP's Master of Science in Software Engineering program, offering opportunities to mentor graduate student teams, draw practicum projects from the cluster's atlas work, and engage with the program's professional curriculum. The position also carries a distinctive responsibility in the current moment: as AI-assisted development matures, the engineer at the center of this project will supply the human judgment and anatomical-domain expertise that a production-grade brain atlas platform needs ? using modern AI tools where they accelerate the work, and pushing back, with rigor, on outputs that compromise spatial accuracy, anatomical interpretability, or release readiness.

Position Responsibilities

Lead the adoption of software engineering best practices for the cluster's research software development, including Agile project management (e.g., Scrum or Kanban), code review, automated testing, continuous integration, and release management.
Architect and develop software components of the rat-to-atlas mapping pipeline, including data ingestion, processing, and visualization tools.
Develop the Brain Maps 4.0 and Chemopleth 1.0 software toward robust public releases, building on existing prototypes with latitude to refactor or rebuild as engineering judgment requires, and ensuring interoperability with international neuroinformatics infrastructure such as EBRAINS and with Python-based community tooling such as the BrainGlobe Atlas API.
Collaborate with cluster-hire colleagues in behavioral neuroscience, circuit imaging and mapping, and ML/AI to translate research requirements into reliable software and to support multi-modal data integration and atlas development.
Contribute to peer-reviewed publications and federal grant applications describing the cluster's software, data, and infrastructure outputs, including the open-access digital atlas of brain reward circuits.
Mentor graduate and undergraduate trainees to contribute their efforts to the research pipeline.
Requirements:

Ph.D. in computer science, software engineering, biomedical informatics, computational science, or a related field; or a Master's degree with substantial professional research software engineering experience.
Experience with software engineering best practices, including Agile project management (e.g., Scrum or Kanban), version control, automated testing, code review, and documentation.
Demonstrated experience designing and shipping non-trivial scientific or research software, evidenced by repositories, releases, or production deployments.
Proficiency in Python and at least one additional language relevant to scientific computing or web infrastructure.
Experience mentoring or training undergraduate or graduate students in software engineering practice, including supervision of student software projects.
Demonstrated interest in or experience with neuroscience, biomedical imaging, or related scientific domains.
Demonstrated ability to work in interdisciplinary teams that translate scientific and experimental requirements into reliable software.
Preferred Qualifications

Experience working in academic or research-intensive environments, including open-source scientific projects
Familiarity with neuroscience or biomedical data formats and standards
Track record of independent grant submissions or co-authored funded proposals related to research software, infrastructure, or scientific computing.
Experience with web-based scientific applications, data visualization, or large-image rendering
Experience with containerization (e.g., Docker) and cloud or HPC deployment of scientific software.
Engagement with FAIR data principles and shared neuroscience infrastructure (e.g., EBRAINS, NIH BRAIN Initiative resources)