Phase2 Technology
R&D Engineer/Scientist IV, Lunar and Planetary Laboratory (Part Time)
Phase2 Technology, Tucson, Arizona, United States, 85718
R&D Engineer/Scientist IV, Lunar and Planetary Laboratory (Part Time)
Position Highlights
The University of Arizona's Lunar and Planetary Laboratory (LPL) seeks an R&D Engineer/Scientist IV to join our Planetary Photogrammetry Research focus group (Lunar and Planetary Laboratory Photogrammetry). This 0.49 FTE position is grant funded, begins immediately, and runs through September 30, 2026, with possible extension contingent on the grant’s renewal. You will apply engineering principles and photogrammetric expertise to design, develop, and implement validation frameworks for planetary topographic data, working independently to interpret stakeholder requirements, create innovative technical solutions, and ensure datasets meet mission‑critical specifications through rigorous quality assurance methodologies. This position is subject to federal ITAR/EAR regulations.
Duties & Responsibilities
Interpret contract specifications and stakeholder needs to develop validation strategies.
Apply engineering problem‑solving methodologies to determine appropriate validation approaches, including statistical sampling methods, error propagation analysis, and uncertainty quantification techniques.
Design multi‑stage validation workflows that incorporate photogrammetric error theory, geodetic principles, and spatial analysis methodologies.
Develop algorithms and processing chains that assess data quality through metrics such as vertical accuracy, horizontal accuracy, spatial resolution compliance, and geometric consistency.
Construct automated validation pipelines using engineering best practices, including modular design, version control, and documented interfaces.
Develop Python scripts and toolchains that integrate GDAL libraries, GIS processing engines (ArcGIS, QGIS, or ENVI), and custom analytical modules to perform:
Geometric accuracy assessments using control point analysis.
Statistical quality metrics, including root mean square error (RMSE), standard deviation, and confidence intervals.
Co‑registration accuracy between overlapping datasets.
Slope, aspect, and roughness consistency checks.
Edge detection and artifact identification.
Comparison algorithms for multi‑source data fusion validation.
Operate a secure cloud computing workstation equipped with specialized image processing and GIS software packages, and interface with data storage systems to access large‑scale planetary datasets.
Utilize stereo photogrammetry software tools and terrain analysis packages to generate reference datasets for validation comparisons.
Configure and optimize software environments to handle complex geospatial data processing workflows.
When existing validation methods prove insufficient, apply engineering principles to design novel solutions, including new algorithmic approaches, custom visualization tools, or statistical methods to quantify uncertainty in planetary terrain models.
Engineer validation procedures to be fully repeatable with quantifiable, objective metrics.
Design automated testing protocols that eliminate subjective assessments and provide consistent, documented quality measures suitable for contract compliance verification.
Execute the validation workflows on planetary topographic products, including Digital Elevation Models (DEMs) and orthorectified image mosaics.
Perform comparative analyses against existing reference datasets (such as LOLA or regional terrain models).
Operate GIS and image processing software to conduct spatial analysis, profile extraction, difference mapping, and statistical assessments.
Identify and categorize data quality issues, including geometric distortions, processing artifacts, datum inconsistencies, or areas failing accuracy specifications.
Document findings with quantitative metrics and provide detailed technical feedback to data providers regarding specific deficiencies and recommended corrections.
Prepare comprehensive technical documentation describing validation methodologies, including mathematical formulations, algorithmic logic, software configurations, and quality control procedures.
Contribute to monthly progress reports detailing validation results, statistical summaries, and technical assessments.
Support supervisor presentations to contract stakeholders with data visualizations, accuracy reports, and compliance documentation.
Conduct PDS‑style peer reviews of topographic data products.
Prepare dataset‑specific summary reports documenting validation outcomes and certification of contract compliance.
Knowledge, Skills, and Abilities
Knowledge of engineering principles, including systems analysis, algorithm design, and quantitative problem‑solving.
Knowledge of Planetary Data System (PDS) geospatial data standards and requirements.
Knowledge of photogrammetric principles, including stereo triangulation, bundle adjustment, error propagation, and accuracy assessment.
Skill in analyzing and working with planetary topographic datasets such as DEMs, DTMs, and point clouds.
Skill in generating topographic data using tools such as Ames Stereo Pipeline, SOCET SET, SOCET GXP, SPC, shape‑from‑shading, or similar photogrammetric software.
Skill in using GDAL, GIS software (ArcGIS, QGIS, ENVI), and scripting languages like Python for geospatial data processing.
Skill in using version control systems and applying software development best practices.
Ability to design, code, and implement automated data processing workflows.
Ability to perform analytical and statistical assessments for quantitative data quality evaluation.
Minimum Qualifications
Bachelor’s degree in physical sciences, engineering, computer science, or equivalent professional experience.
Minimum of 8 years of relevant work experience, or equivalent combination of education and experience.
Preferred Qualifications Advanced degree preferred.
Contact Information Dr. Sarah Sutton ssutton@lpl.arizona.edu
Application Documents Resume and Cover Letter
#J-18808-Ljbffr
Duties & Responsibilities
Interpret contract specifications and stakeholder needs to develop validation strategies.
Apply engineering problem‑solving methodologies to determine appropriate validation approaches, including statistical sampling methods, error propagation analysis, and uncertainty quantification techniques.
Design multi‑stage validation workflows that incorporate photogrammetric error theory, geodetic principles, and spatial analysis methodologies.
Develop algorithms and processing chains that assess data quality through metrics such as vertical accuracy, horizontal accuracy, spatial resolution compliance, and geometric consistency.
Construct automated validation pipelines using engineering best practices, including modular design, version control, and documented interfaces.
Develop Python scripts and toolchains that integrate GDAL libraries, GIS processing engines (ArcGIS, QGIS, or ENVI), and custom analytical modules to perform:
Geometric accuracy assessments using control point analysis.
Statistical quality metrics, including root mean square error (RMSE), standard deviation, and confidence intervals.
Co‑registration accuracy between overlapping datasets.
Slope, aspect, and roughness consistency checks.
Edge detection and artifact identification.
Comparison algorithms for multi‑source data fusion validation.
Operate a secure cloud computing workstation equipped with specialized image processing and GIS software packages, and interface with data storage systems to access large‑scale planetary datasets.
Utilize stereo photogrammetry software tools and terrain analysis packages to generate reference datasets for validation comparisons.
Configure and optimize software environments to handle complex geospatial data processing workflows.
When existing validation methods prove insufficient, apply engineering principles to design novel solutions, including new algorithmic approaches, custom visualization tools, or statistical methods to quantify uncertainty in planetary terrain models.
Engineer validation procedures to be fully repeatable with quantifiable, objective metrics.
Design automated testing protocols that eliminate subjective assessments and provide consistent, documented quality measures suitable for contract compliance verification.
Execute the validation workflows on planetary topographic products, including Digital Elevation Models (DEMs) and orthorectified image mosaics.
Perform comparative analyses against existing reference datasets (such as LOLA or regional terrain models).
Operate GIS and image processing software to conduct spatial analysis, profile extraction, difference mapping, and statistical assessments.
Identify and categorize data quality issues, including geometric distortions, processing artifacts, datum inconsistencies, or areas failing accuracy specifications.
Document findings with quantitative metrics and provide detailed technical feedback to data providers regarding specific deficiencies and recommended corrections.
Prepare comprehensive technical documentation describing validation methodologies, including mathematical formulations, algorithmic logic, software configurations, and quality control procedures.
Contribute to monthly progress reports detailing validation results, statistical summaries, and technical assessments.
Support supervisor presentations to contract stakeholders with data visualizations, accuracy reports, and compliance documentation.
Conduct PDS‑style peer reviews of topographic data products.
Prepare dataset‑specific summary reports documenting validation outcomes and certification of contract compliance.
Knowledge, Skills, and Abilities
Knowledge of engineering principles, including systems analysis, algorithm design, and quantitative problem‑solving.
Knowledge of Planetary Data System (PDS) geospatial data standards and requirements.
Knowledge of photogrammetric principles, including stereo triangulation, bundle adjustment, error propagation, and accuracy assessment.
Skill in analyzing and working with planetary topographic datasets such as DEMs, DTMs, and point clouds.
Skill in generating topographic data using tools such as Ames Stereo Pipeline, SOCET SET, SOCET GXP, SPC, shape‑from‑shading, or similar photogrammetric software.
Skill in using GDAL, GIS software (ArcGIS, QGIS, ENVI), and scripting languages like Python for geospatial data processing.
Skill in using version control systems and applying software development best practices.
Ability to design, code, and implement automated data processing workflows.
Ability to perform analytical and statistical assessments for quantitative data quality evaluation.
Minimum Qualifications
Bachelor’s degree in physical sciences, engineering, computer science, or equivalent professional experience.
Minimum of 8 years of relevant work experience, or equivalent combination of education and experience.
Preferred Qualifications Advanced degree preferred.
Contact Information Dr. Sarah Sutton ssutton@lpl.arizona.edu
Application Documents Resume and Cover Letter
#J-18808-Ljbffr