Assistant Research Professor - Data Analysis and Remote Sensing

University of Idaho - Moscow, ID

Part-time,Full-time - Entry Level

Moscow, ID

Educational Services

About the position

The University of Idaho is seeking a non-tenure track Assistant Research Professor specializing in Data Analysis and Remote Sensing within the Department of Forest, Rangeland and Fire Sciences (FRFS). This position is designed for an individual with a strong background in computational data analysis, particularly in the context of developing quantitative remote sensing techniques for environmental monitoring. The successful candidate will engage in research that utilizes both passive (optical) and active (LiDAR) sensors, focusing on the creation of quality assessment and validation standards for remotely sensed thematic products. This role is critical in expanding the interdisciplinary research and teaching efforts of the department, which aims to address pressing environmental issues through innovative data analysis techniques. As a soft-funded position, the candidate is required to secure their own salary and fringe benefits, with at least seven months of salary support needed at the time of application. The role demands a proactive approach to research funding, as the candidate will be expected to compete for external funding opportunities to support their work. The ideal candidate will have a Ph.D. in Mathematics or a related field, with a strong emphasis on mathematical principles applicable to environmental science. They should also possess experience in statistical analysis, programming, and publishing in high-impact journals, as well as familiarity with the CEOS guidelines for validation and calibration of thematic map products. In addition to research responsibilities, the Assistant Research Professor will be expected to contribute to teaching and mentoring within the department, particularly at the undergraduate and graduate levels. The position offers an exciting opportunity to work within a collaborative research environment, leveraging advanced technologies and methodologies to address complex environmental challenges.

Responsibilities

• Conduct interdisciplinary research in computational data analysis and remote sensing techniques.
• Develop quantitative remote sensing techniques for environmental monitoring using passive and active sensors.
• Establish quality assessment and validation standards for remotely sensed thematic products.
• Secure external funding to support research activities and salary.
• Publish research findings in high-impact, refereed journals.
• Collaborate with interdisciplinary research teams and contribute to teaching efforts.
• Mentor graduate students at the PhD level.

Requirements

• Completed Ph.D. in Mathematics or a related field (Engineering or Physics with a strong mathematical focus).
• Experience with natural resource and environmental science issues.
• Knowledge of statistics, sampling theory, and probability theory.
• Proficiency in compiled and interpreted programming languages, specifically C and Python in a Linux environment.
• Experience publishing research in high-impact, refereed journals.
• Demonstrated ability to compete for external funding.
• Familiarity with CEOS guidelines for validation and calibration of thematic map products.
• Experience with machine learning techniques.
• Expertise in geometric data analysis with a focus on 3-D structures.
• Strong written and oral communication skills.
• Self-funding capability with at least 7 months of salary support in-hand at the time of application.

Nice-to-haves

• Experience working as part of an interdisciplinary research team.
• Familiarity with supercomputing environments.
• Strong understanding of artificial intelligence and deep learning frameworks.
• Experience in teaching undergraduate courses.
• Experience mentoring graduate students at the PhD level.