Data Scientist 2-Modeling Dryland Carbon and Water Fluxes

University Of New Mexico•Albuquerque, NM

35d•Onsite

About The Position

The Litvak Lab at the University of New Mexico is hiring a Data Scientist 2 to support cutting-edge machine learning modelling of carbon and water fluxes and scaling to the watershed scale. The candidate will be responsible for processing of in situ ecosystem physiological measurements from ground based eddy covariance towers, and modification of existing model structure and development of novel algorithms to combine in situ ecosystem physiology measurements with gridded remote sensing data streams and products. In addition, the candidate will interact with other members of the FOR-NM team, specifically those doing remote sensing and process-based modelling. One year of funding is available to start with likely extension to two years. See the Position Description for additional information.

Requirements

Bachelor's degree in a relevant field
at least 5 years of experience directly related to the duties and responsibilities specified
Completed degree(s) from an accredited institution that are above the minimum education requirement may be substituted for experience on a year for year basis.

Nice To Haves

M.S. in Electrical and Computer Engineering
Familiarity with multi-modal deep learning architectures in Python
Competency in distributed computing resources and high performance computing environments
Familiarity with ecosystem scale monitoring of in situ carbon, water and energy fluxes
Expertise in computer vision algorithmic design
Familiarity with communicating complex learning and AI concept machine learning to audiences
Familiarity with version control systems and experiment tracking software

Responsibilities

processing of in situ ecosystem physiological measurements from ground based eddy covariance towers
modification of existing model structure and development of novel algorithms to combine in situ ecosystem physiology measurements with gridded remote sensing data streams and products
interact with other members of the FOR-NM team, specifically those doing remote sensing and process-based modelling