•Revised • Postdoctoral Scholar, Interdisciplinary Data Science, College of Engineering [R0144891]

Job Description

The University of Nevada, Las Vegas invites applications for Postdoctoral Scholar, Interdisciplinary Data Science, College of Engineering [R0144891] .

• Revised
• ROLE of the POSITION

The Howard R. Hughes College of Engineering at UNLV invites applications for a full-time, 12-month, Postdoctoral Scholar of Interdisciplinary Data Science, commencing as early as September 1, 2025. The position is renewable for up to two additional years (3 years total), pursuant to availability of funds. This postdoctoral scholar will work on research topics related to AI-Driven Wildfire Detection Using Drones and Remote Sensing; Machine Learning Models for Predicting Wildfire Spread; Wildfire Risk Assessment Through Multi-Modal Data Integration; Automated Vegetation and Fuel Load Mapping Using Computer Vision; AI-Based Wildfire Smoke and Air Quality Monitoring; Deep Learning for Post-Wildfire Damage Assessment.

PROFILE of the OFFICE OF POSTDOCTORAL AFFAIRS (OPA)

The mission of the UNLV Office of Postdoctoral Affairs (OPA) is to enhance and support postdoctoral training and career development; serve as the central resource office for postdoctoral questions and issues for faculty, supervisors, mentors, and prospective and current postdocs; build a community for all UNLV postdocs; and provide professional development programs and networking events for postdocs.

UNLV currently employs postdoctoral scholars across a wide range of disciplines.

MINIMUM QUALIFICATIONS

This position requires a PhD Degree in Computer Science, Electrical Engineering, Environmental Engineering, Geospatial Science, Data Science, Computer Engineering, or a closely related field, with an emphasis on machine learning, AI, remote sensing, computer vision, or interdisciplinary data science applications to environmental challenges such as wildfire management, from an accredited college or university as recognized by the United States Department of Education and / or the Council on Higher Education Accreditation (CHEA) and demonstrated experience in conducting independent research, managing large datasets, and developing advanced analytical models for real-world environmental applications. Credentials must be obtained prior to the start date.

COMMITMENT to DIVERSITY and CAMPUS VALUES

A successful candidate will support diversity, equity, and inclusiveness and contribute to a respectful, positive work environment. They will use our to guide their decisions and actions and demonstrate our Rebel spirit.

PREFERRED QUALIFICATIONS

Experience applying machine learning (ML) or artificial intelligence (AI) techniques, especially to environmental data.

Proficiency in remote sensing and geospatial data analysis.

Expertise in computer vision and image analysis, particularly for drone or satellite imagery.

Familiarity with wildfire modeling, risk assessment, or disaster management.

Strong programming skills in Python, Matlab, R, or other relevant languages.

Experience working with large datasets and high-performance computing (HPC) systems.

Proven track record of interdisciplinary collaboration on research projects.

Strong publication record in relevant fields such as environmental science, data science, or engineering.

Excellent communication and teamwork skills.

Prior experience mentoring or supervising graduate students or research assistants.

SALARY RANGE

Salary competitive with those at similarly situated institutions. Position is contingent upon funding.

Comprehensive health insurance including medical, dental, and vision

No state income tax (if applicable)

Comprehensive retirement plan and voluntary benefits programs

Grant in Aid waives the non-resident fee for the employee and their eligible dependents at Nevada System of Higher Education (NSHE) schools

PERKS

Employee recognition and appreciation programs

Discounted tickets for UNLV athletics

Discounted tickets for and

discounts on and off campus

for all UNLV faculty and staff at no cost