Temporary Research Scientist/Engineer 3

University of Washington Medical Center•Seattle, WA

23h•$80,244 - $87,444

About The Position

The Civil and Environmental Engineering department has an outstanding opportunity for a temporary Research Scientist/Engineer 3 to join their team. 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.

Requirements

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
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 modeling.
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.
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.
Strong written and oral communication skills.
Record of peer-reviewed scientific publication appropriate to career stage.

Nice To Haves

PhD in environmental engineering, atmospheric science, applied mathematics, statistic, 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 questions of 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.

Responsibilities

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.