Assistant Research Professor Climate Change

The University of Maine is seeking an Assistant Research Professor in Climate Change, focusing on cryosphere and land-surface modeling. This full-time, on-site position involves field observations and modeling related to snow properties and meltwater effects on land stability. The role includes collaboration with various institutions and may involve teaching opportunities. The position is funded through a multi-year Department of Defense contract, starting in summer 2025.
Assistant Research Professor
Orono, ME, United States (On-site)
Job Info
Job Identification
939
Job Category
Faculty

Locations
College Avenue, Orono, ME, 04469, US
(On-site)
Business Unit
University of Maine
Job Schedule
Full time
Regular or Temporary
Regular
Department
UM Climate Change Institute
Bargaining Unit
Associated Faculties of the University of Maine System (AFUM)
Job Description
The University of Maine seeks to hire a Research Assistant Professor within the Climate Change Institute with an additional affiliation in the School of Earth & Climate Sciences. The position will have a concentration in cryosphere and land-surface evolution modeling, near-surface cryosphere geophysics (radar and/or seismology experience preferred), or cryosphere terrestrial/airborne radar remote sensing. This position is funded by a multi-year Department of Defense contract which is renewable annually through June of 2028 contingent upon successful completion of annual project deliverables. The overall project goal is focused on characterizing snow properties and quantifying snowmelt, meltwater runoff, and meltwater influence on land stability. The person selected for this position will help collect and couple field observations with hybrid physics-based/machine learning modeling. The person selected for this position should be open to collaborating with others within the University of Maine, the multiple collaborating universities, the Cold Regions Research and Engineering Laboratory, and the Foundation for Glacier and Environmental Research – Juneau Icefield Research Program (JIRP), to fill the needs of this project. The candidate would lead or co-lead a specific component of the project, related to one of the following topics:

Numerical modeling of Earth surface evolution and land stability over snow-covered regions.
Acquiring, analyzing, and interpreting terrestrial radar interferometry, LiDAR, or other remote sensing data in effort to study snow properties and land stability and snow/meltwater/land interactions.
Field study locations for this project include the Juneau Icefield (Alaska and Canada), Canwell Glacier (Alaska), and sites within northern Maine and New Hampshire. Both the Climate Change Institute and the School of Earth & Climate Sciences emphasize coupled field-analytical-modeling approaches to research, their faculty and students closely collaborate with several independent organizations, state agencies, and federal agencies to study local to global issues of importance in Earth systems.

We expect employment to begin during the summer of 2025 so the team member can participate in the 2025 Juneau Icefield research season. This position is a 100% research appointment. However, there are potential opportunities for the selected candidate to teach courses through the University of Maine on a contract basis. In addition, the candidate is eligible to supplement their funded salary on this contract via additional research funding up to a total of 12 months of salary per year. We intend to develop a robust mentoring program for the incoming faculty member to help them successfully secure additional funding for their position. This is a non-tenure track, academic year position.

About the University:

The University of Maine is a community of more than 11,900 undergraduate and graduate students, and 2,500 employees located on the Orono campus, the regional campus in Machias, and throughout the state. UMaine is a land, sea and space grant university, and maintains a leadership role as the University of Maine System's flagship institution. UMaine is the state's public research university and a Carnegie R1 top-tier research institution, dedicated to providing excellent teaching, research and service for Maine, the nation and the world. More information about UMaine is at umaine.edu.

The University of Maine offers a wide range of benefits for employees including, but not limited to, tuition benefits (employee and dependent), comprehensive insurance coverage including medical, dental, vision, life insurance, and short and long term disability as well as retirement plan options. As a former NSF ADVANCE institution, the University of Maine is committed to diversity in our workforce and to dual-career couples.

UMaine is located in beautiful Central Maine. Many employees report that a primary reason for choosing to come to UMaine is quality of life. Numerous cultural activities, excellent public schools, safe neighborhoods, high quality medical care, little traffic, and a reasonable cost of living make the greater Bangor area a wonderful place to live. Learn more about what the Bangor region has to offer here.

Qualifications:
Required:

Ph.D. in geophysics, geodynamics, remote sensing, Earth Systems Science, or a related field by the time of hire.
Experience working with and building cryosphere or landscape evolution numerical models.
Experience with development and application of machine learning to cryosphere or Earth system models.
A record of peer-reviewed publications.
Potential to build an extramurally funded research program.
Evidence of interest and ability to promote diversity, equity, and inclusion within the institution and the discipline.
Strong communication skills.
Preferred:

Experience collecting, processing, and interpreting terrestrial radar interferometry data.
Experience collecting, processing, and interpreting LiDAR data.
Experience collecting, processing, and interpreting electromagnetic and/or seismic data.
Experience working in Polar glacier or mountain environments.
Experience using CROCUS or SNOWPACK numerical models.
Proficiency using Python and Matlab for numerical modeling.