Temporary Research Scientist​/Engineer

Seattle, WA

Full time

REQ-

Reporting to Professor Julian Marshall, this position will hold the title of Research Scientist and will conduct research on high-resolution air pollution measurement, modeling, and source characterization.

• Develop, implement, evaluate, and advance computational approaches for estimating spatially and temporally resolved air-pollutant emissions and exposures from observational data.
• Work 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.

• 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 area relevant to the research, 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
• Applicants who do not meet these qualifications WILL NOT be forwarded to the Hiring Manager.

• Demonstrated ability to conduct quantitative computational research.
• Strong scientific programming skills in Python or a comparable scientific-computing environment (e.g., R, Julia).
• Strong quantitative reasoning and a foundation in applied mathematics, statistics, numerical methods, or computational…