Northern Arizona University
Postdoctoral Scholar, Gurney Lab
Northern Arizona University, Flagstaff, Arizona, United States, 86004
Overview
Postdoctoral Scholar, Gurney Lab
at
Northern Arizona University
in Flagstaff, AZ. The NAU Gurney Lab conducts cutting-edge research on quantifying greenhouse gas (GHG) emissions at multiple scales—from the building to the globe—using a machine learning approach to build a multiscale, high-resolution GHG emissions model. This work complements the existing emissions modeling systems Vulcan and Hestia developed in NAU's Gurney Lab over the past 20 years. Responsibilities
Data collection and pre-processing of ML model input data (remotely sensed, infrastructure, atmospheric data, etc.), developing/improving machine learning model for flux estimation, testing, different models, creating diagnostics for different models and input data sets. Postdoctoral scientist will be responsible for producing a high-resolution native resolution and regular gridded output. Review literature and identify appropriate approaches and develop peer-reviewed publications reflecting research outcomes. Software Development - development of new codebase environment that encompasses data ingestion, pre-processing, machine learning, and structured output. Outreach and Communication - outreach and communication of results at scientific meetings. Co PI Responsibilities - Postdoc is anticipated to contribute to proposals, ideally as a co-PI. Other duties as assigned - Supervision of graduate student research and other team members. Minimum Qualifications
Ph or equivalent doctorate in relevant field is required (e.g. Computer Science, Informatics, Civil Engineering, Urban Planning, Data Science) from an accreditated university by the date of appointment. Preferred Qualifications
Experience in Multiyear numerical analysis Minimum of three years experience in use of machine learning in science applications Minimum of two years experience in use of remote sensing products (trace gas, land cover, optical/IR) Multiyear R and/or Python Programming Multiyear experience wokring with remote sensing data products Experience working in Linux environment with high performance computer Experience working in a team environment with shared workflow (code, data, analysis) Experience with infrastructure/energy data Experience in quantification of GHG emissions Knowledge, Skills, & Abilities
Knowledge of working with GHG flux reporting/quanitification and analysis Use of GIS in context of urban data such as building layers, road layers, and other infrastructural or socio-demographic data Development, authorship, and understanding of medium-size R scripts
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Postdoctoral Scholar, Gurney Lab
at
Northern Arizona University
in Flagstaff, AZ. The NAU Gurney Lab conducts cutting-edge research on quantifying greenhouse gas (GHG) emissions at multiple scales—from the building to the globe—using a machine learning approach to build a multiscale, high-resolution GHG emissions model. This work complements the existing emissions modeling systems Vulcan and Hestia developed in NAU's Gurney Lab over the past 20 years. Responsibilities
Data collection and pre-processing of ML model input data (remotely sensed, infrastructure, atmospheric data, etc.), developing/improving machine learning model for flux estimation, testing, different models, creating diagnostics for different models and input data sets. Postdoctoral scientist will be responsible for producing a high-resolution native resolution and regular gridded output. Review literature and identify appropriate approaches and develop peer-reviewed publications reflecting research outcomes. Software Development - development of new codebase environment that encompasses data ingestion, pre-processing, machine learning, and structured output. Outreach and Communication - outreach and communication of results at scientific meetings. Co PI Responsibilities - Postdoc is anticipated to contribute to proposals, ideally as a co-PI. Other duties as assigned - Supervision of graduate student research and other team members. Minimum Qualifications
Ph or equivalent doctorate in relevant field is required (e.g. Computer Science, Informatics, Civil Engineering, Urban Planning, Data Science) from an accreditated university by the date of appointment. Preferred Qualifications
Experience in Multiyear numerical analysis Minimum of three years experience in use of machine learning in science applications Minimum of two years experience in use of remote sensing products (trace gas, land cover, optical/IR) Multiyear R and/or Python Programming Multiyear experience wokring with remote sensing data products Experience working in Linux environment with high performance computer Experience working in a team environment with shared workflow (code, data, analysis) Experience with infrastructure/energy data Experience in quantification of GHG emissions Knowledge, Skills, & Abilities
Knowledge of working with GHG flux reporting/quanitification and analysis Use of GIS in context of urban data such as building layers, road layers, and other infrastructural or socio-demographic data Development, authorship, and understanding of medium-size R scripts
#J-18808-Ljbffr