U

University of Washington

Public research university in Seattle, WA

Research Scientist/Engineer 3

Full-TimeUpdated on 10/23/2026Deadline 10/26/26
$80.2k - $87.4k/yr
Mid
Bachelor's, PhD
Seattle, WA, USA
In Person
Architecture & Civil EngineeringEnvironmental Engineering

About the job

Requirements
  • A bachelor's degree in environmental engineering, atmospheric science, applied mathematics, statistics, physics, computer science, geosciences, chemical engineering, or another closely related quantitative field.
  • Four years of experience in at least one relevant research area, such as data assimilation, optimization, inverse problems, Bayesian methods, uncertainty quantification, numerical modeling, atmospheric transport or dispersion, source-receptor modeling, spatial statistics, or spatiotemporal modeling.
  • Demonstrated ability to conduct quantitative computational research.
  • Strong scientific programming skills in Python or a comparable scientific-computing environment such as R or Julia.
  • Strong quantitative reasoning and a foundation in applied mathematics, statistics, numerical methods, or computational modeling.
  • Experience in at least one relevant research area, such as data assimilation, optimization, inverse problems, Bayesian methods, uncertainty quantification, numerical modeling, atmospheric transport or dispersion, source-receptor modeling, spatial statistics, or spatiotemporal modeling.
  • Demonstrated ability to formulate technical problems, implement computational approaches, evaluate results critically, and troubleshoot complex scientific or computational problems.
  • Ability to learn unfamiliar quantitative methods and scientific concepts and apply them to new research problems.
  • Ability to independently move substantial research tasks forward with limited day-to-day supervision while working effectively within a collaborative research team.
  • Record of peer-reviewed scientific publication appropriate to career stage.
Responsibilities
  • Develop, implement, evaluate, and advance computational approaches for estimating spatially and temporally resolved air-pollutant emissions and exposures from observational data.
  • Use methods that may include statistical or Bayesian inverse modeling, data assimilation, optimization, uncertainty quantification, numerical methods, and related approaches.
  • Develop methods for integrating observed pollutant concentrations with prior emissions information, meteorological information, and representations of atmospheric transport.
  • Investigate alternative model formulations, identify methodological limitations, develop solutions to technical problems, and improve model accuracy, robustness, scalability, and interpretability.
  • Develop and maintain reproducible scientific software and computational workflows.
  • Analyze high-resolution environmental datasets, including mobile-monitoring and possibly fixed-site air-quality observations.
  • Develop and apply methods for combining measurements that differ in spatial and temporal coverage, sampling frequency, and uncertainty.
  • Evaluate spatial and temporal patterns in pollutant concentrations, elevated-concentration locations, source influences, and variability across locations and time.
  • Develop approaches for translating heterogeneous monitoring observations into inputs or observational constraints suitable for quantitative modeling and inference.
  • Contribute to the design of high-resolution mobile-monitoring studies.
  • Evaluate tradeoffs among spatial and temporal coverage, repeat sampling, route length, driving time, platform availability, seasonal and time-of-day coverage, and other practical constraints.
  • Develop and evaluate quantitative approaches for route design, allocation of monitoring effort, sampling representativeness, and optimization of monitoring resources.
  • Assess how alternative sampling designs affect the information that can be obtained about pollution concentrations, sources, emissions, and exposures.
  • Design and conduct numerical experiments, sensitivity analyses, and validation studies.
  • Evaluate model performance, robustness, identifiability, sensitivity to prior assumptions, observational requirements, and uncertainty.
  • Develop cross-validation, out-of-sample evaluation, or other performance-assessment approaches.
  • Assess how monitoring density, measurement frequency, spatial coverage, and combinations of observational datasets affect model performance and inference.
  • Collaborate with faculty investigators, research staff, students, government-agency scientists, and external technical partners.
  • Interpret research findings and communicate methodological choices, assumptions, limitations, and results.
  • Contribute to peer-reviewed manuscripts, technical reports, presentations, research proposals, project documentation, and meetings.
  • Participate in research planning and recommend methodological or analytical directions within assigned areas of responsibility.
Desired Qualifications
  • A PhD in environmental engineering, atmospheric science, applied mathematics, statistics, physics, computer science, geosciences, chemical engineering, or another closely related quantitative field.
  • Demonstrated ability to conduct quantitative computational research.
  • Experience with data assimilation, optimization, Bayesian inference, inverse modeling, or related quantitative methods.
  • Experience with atmospheric transport, dispersion, trajectory, or source-receptor modeling.
  • Experience working with mobile air-pollution monitoring data or other high-frequency geospatial environmental measurements.
  • Experience with spatial or spatiotemporal analysis, geospatial data, integration of observations across spatial or temporal scales, or methods for estimating pollutant emissions.
  • Experience designing environmental monitoring or sampling studies, including representativeness, repeated measurements, spatial coverage, temporal coverage, or optimization under resource constraints.
  • Familiarity with air-pollution measurement and pollutants such as particulate matter, black carbon, ultrafine particles, nitrogen oxides, volatile organic compounds, or related pollutants.
  • Experience developing reproducible research software, working with large scientific datasets, or using high-performance computing.
  • Experience with air-pollution exposure assessment, source attribution, or environmental-health applications.

About the company

U

University of Washington

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The University of Washington is a public research university founded in 1861, with campuses in Seattle, Bothell, and Tacoma. It provides undergraduate, graduate, and professional education alongside research and public service.

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