Research Asst Professor - Rudgers

The University of New Mexico•Albuquerque, NM

5d•Onsite

About The Position

The Assistant Research Professor will conduct research under a National Science Foundation (NSF) Science and Technology for Advanced Research (STAR) award investigating the ecological and evolutionary mechanisms by which soil fungal microbiomes enhance plant adaptation to drought. The position will lead greenhouse, laboratory, and field studies examining plant–fungal interactions, microbiome diversity, and the mechanisms underlying microbe-mediated adaptation to environmental stress. The Sevilleta LTER position will constitute a portion of the FTE but otherwise remain the same as the current responsibilities to work on projects of the Sevilleta Long-Term Ecological Research Program (LTER) as described in the proposal for the state of New Mexico Technology Enhancement Fund (0480YV). This work includes using the Terrestrial Ecosystem Model (TECO) together with field data and the plant traits database of the Sevilleta LTER program to project future dynamics of carbon. Work will also involve development of new publicly available interface for this project, EcoPAD - Modeling Platform for Dryland Forecasting. This involves technology enhancements that include automated data assimilation algorithms for wireless remote sensor data and phenocameras, data-driven modeling approaches to synthesize massive data, and software development.

Requirements

Ph.D. in Ecology, Evolutionary Biology, Plant Biology, Microbiology, Environmental Science, or a closely related field.
Research experience in ecology, evolutionary biology, plant biology, microbial ecology, or plant–microbe interactions.
Experience designing and conducting laboratory, greenhouse, and/or field experiments.
Experience managing and analyzing scientific data using quantitative and statistical methods.
Demonstrated ability to communicate research findings through scientific publications, reports, presentations, or other professional products.
Ability to conduct research independently and collaborate effectively with faculty, staff, students, and external research partners.

Nice To Haves

Postdoctoral research experience and a demonstrated record of peer-reviewed publications in ecology, evolutionary biology, plant biology, microbial ecology, or a related field.
Demonstrated expertise in plant–soil microbiome interactions, fungal ecology, mutualism, the evolution of cooperation, biodiversity–ecosystem functioning, or plant responses to drought.
Experience conducting experiments involving plant propagation, fungal isolation and culturing, microbial inoculation, plant physiological or fitness measurements, and/or microbiome characterization.
Experience with advanced statistical analysis and scientific programming in R
Mentoring undergraduate, graduate, or community college students
Coordinating collaborative or externally funded research projects.
A demonstrated commitment to cultivate an understanding of the rich and varied cultures of New Mexico and to the success of the university's mission to serve local and global communities.

Responsibilities

Experimental design and implementation
Fungal isolation, culturing, inoculation, and trait characterization
Plant propagation and physiological measurements
Collection, management, and statistical analysis of ecological and microbiome data
Preparation of manuscripts, technical reports, and grant deliverables
Presentation of research findings at scientific meetings
Mentoring undergraduate and community college student researchers
Coordinating collaborations with faculty and external research partners
Ensuring compliance with institutional and sponsor requirements
Assisting with the development of future externally funded research programs
Work on projects of the Sevilleta Long-Term Ecological Research Program (LTER)
Using the Terrestrial Ecosystem Model (TECO) together with field data and the plant traits database of the Sevilleta LTER program to project future dynamics of carbon
Development of new publicly available interface for this project, EcoPAD - Modeling Platform for Dryland Forecasting
Technology enhancements that include automated data assimilation algorithms for wireless remote sensor data and phenocameras, data-driven modeling approaches to synthesize massive data, and software development