R&D Engineer/Scientist IV, Lunar and Planetary Laboratory (Part Time)

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 a grant funded position that begins immediately and runs through September 30, 2026. An extension may be possible contingent on the grant's renewal.

In this role, you'll apply engineering principles and photogrammetric expertise to design, develop, and implement validation frameworks for planetary topographic data. You'll work 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.

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• 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 (such as
  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. 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. This may include developing new algorithmic approaches to
  detect specific data anomalies, creating custom visualization tools to
  identify systematic errors, or engineering 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 (e.g., 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.

This job posting reflects the general nature and level of work expected of the selected candidate(s). It is not intended to be an exhaustive list of all duties and responsibilities. The institution reserves the right to amend or update this description as organizational priorities and institutional needs evolve.

• 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.