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Remote Sensing Algorithm Research Postdoctoral Scholar

University of California, Davis

Job Description

Open date: September 21, 2022

Next review date: Friday, Oct 21, 2022 at 11:59pm (Pacific Time)
Apply by this date to ensure full consideration by the committee.

Final date: Saturday, Dec 31, 2022 at 11:59pm (Pacific Time)
Applications will continue to be accepted until this date, but those received after the review date will only be considered if the position has not yet been filled.


Dr. Susan Ustin’s Center for Spatial Technologies and Remote Sensing (CSTARS) at UC Davis is known for nearly 30 years as one of strongest environmental remote sensing groups in the United States, with a decade-long expertise in developing, prototyping, and operating automated satellite data processing algorithms and systems to support ecosystem and fire management and science (

CSTARS faculty and staff scientists come from all over the world and variety of disciplines, including GIS, Computer Science, Applied Math, Geography, Botany, Ecology, Soil, Atmospheric and Environmental Sciences. We foster flexible, empowering, inclusive, and enjoyable work environment and constantly strive to make best available science better.

We are looking for a highly motivated and organized Postdoctoral Scientist (potentially more than one position) with excellent quantitative, computer programming, and communication skills who will develop explainable cutting-edge methods and reproducible GIS datasets to more fully characterize the 50+ year history of ecosystem change in California, using dense time series of Landsat 4-9. The new developments are expected to be coupled or integrated with the existing capabilities of the Ecosystem Disturbance and Recovery Tracker (eDaRT) — a highly automated satellite image processing and analysis system currently operated by the US Forest Service — and deployed at scale to improve water resource assessment and land management in California and potentially US-wide. The Postdoc is expected to be able to work independently on complex assignments with assistance from team members while following general guidance from PI’s and science/engineering principles.

This position under a high-stakes project funded by the State of California will involve close collaboration with senior scientists and software engineers at CSTARS on the eDaRT team, UC Davis hydrologists, and our federal and state agency partners and sponsors, providing excellent opportunities for rapid professional growth, career development, and a future leadership potential.

Major Responsibilities:

  • Know, evaluate, and compare state-of-the-art approaches and models, as related to the project.
  • Develop robust and automated Landsat (TM/ETM/OLI-TIRS) based methods and geospatial products addressing project-specific and broader needs of land and water management science and practice, by following Agile development approaches, such as continuous integration and continuous delivery, in collaboration with other team members. Examples of the research areas and needs include: a) improved detection of landcover change and disturbance in various ecosystems; b) characterization/attribution of ecosystem change (type/cause, magnitude, etc.); and/or c) characterizing pre- and post-disturbance vegetation properties and their temporal trajectories, such as subpixel cover fractions, LAI, vegetation types, canopy stress, land-cover/land-use categories and their transition classes, and potentially other characteristics, as needed.
  • Develop methods and geospatial products to detect and characterize land change and ecosystem disturbances and their impacts using historic Landsat Multi-Spectral Scanner (MSS) imagery.
  • Implement developed methods and models as reusable and well-documented research prototype programs/modules in an interpreted language, such as MATLAB, Python, JavaScript, IDL, or R.
  • Design and regularly perform model validation experiments; develop/optimize and automate training/test data collection processes, e.g. based on interpretation of high-resolution imagery and ancillary data. Develop and maintain training and test datasets.
  • Continuously document for shared internal use, in a clear and well-organized manner, all ongoing research activities, intermediate and final datasets, methods, experiments, results, and code; lead co-authored peer-reviewed publications, prepare and personally deliver conference/workshop presentations, and assist in the preparation of reports to sponsor.
  • Strive to comply with the project schedule, regularly track and communicate the progress, recognize potential issues early, and take initiative to seek guidance/assistance, in a timely manner.
  • Assist in the completion of collaborative projects in the lab and generate results for use in new grant submissions.

Specific work scope items and their priorities will be collaboratively determined based on the project needs, skill sets of available personnel on the team, and other factors.

  • Wear personal protective equipment (PPE) and follow all health and safety protocols in the laboratory, field and on campus as required.
  • Submit daily symptom tracking as required.
  • This position requires sitting at a computer workstation for extended periods of time, operating and occasional moving computer and peripheral equipment; communication in person, telephone and e-mail, and other online services; reading program output and a variety of computer displays. Move and manipulate supplies and equipment of various weights up to 20 lbs.
  • Work hours are normally Monday – Friday from 8:00am to 5:00pm or 9:00am to 6:00pm, but with flexibility to alter working hours to accommodate field data collection or because of specific project needs.
  • Ability to work effectively in a shared workspace environment. This is not a fully remote position.
  • Occasional travel to designated field sites of project, to meetings with State Agency personnel or to professional meetings/conferences.

Commensurate on experience level with a minimum salary of $55,632, benefits included for 12 months duration appointment at full time of 100%. 24 work days of paid time off per year, 12 days of sick leave per year and 14 paid University holidays per year.

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Provide required information and upload documents
If any, provide required reference information

  • Curriculum Vitae (up to 5 pages, not strictly) including contact information for at least two references.
  • Cover Letter
  • List of Publications and Presentations
  • A brief Statement of Career/Research Interests and Goals (preferably 1-2 pages).
  • Up to three representative publications in English language.
  • Statement of Contributions to Diversity - Diversity contributions documented in the application file will be used to evaluate applicants. Visit


Basic qualifications (required at time of application)

A. Ph. D. degree in Remote Sensing, Geographic Information Systems, Geography, Ecology, Statistics, Computer Science, Physics, Engineering, Applied Math, or a similar field.

B. Strong quantitative background that is necessary to comprehend, evaluate, and implement advanced remote sensing concepts, models and algorithms, including the ability to understand and communicate advanced quantitative concepts and methods through appropriate use of scientific terms (in English), notations, and formulas.

C. Excellent problem-solving skills.

D. Broad knowledge and proven record of development of advanced Machine-Learning/ Statistical /Probabilistic models and algorithms (preferably for remote sensing) for at least TWO of these general problem types:
  • Detection of changes and anomalies in observational data across space and/or time.
  • Classification of high-dimensional data.
  • Modeling and analyses of observation time series/ trajectories in the presence of stochastic noise.
  • Feature selection/ extraction/ dimensionality reduction.
  • Prediction/forecasting/survival analysis.

E. Evidence of understanding and following sound practices of model building, training, and accuracy assessment, including sample design and control for model complexity.

F. Advanced Technical Skills:
  • Computer Programming: MATLAB, Python, R, JavaScript, IDL or similar, with a minimum of 3 (THREE) years of combined experience applied to remote sensing image processing, data science, or methodologically similar problems.
  • Experience with algorithm development in MATLAB environment is a plus. (3+ years of programming in MATLAB is a big plus). Solid command of multiple programming languages (e.g. MATLAB + Python + R) is a plus.
  • Experience with ArcGIS, ENVI, or similar GIS/Remote Sensing software. Ability to efficiently perform complex geo-processing of imagery and manipulate large datasets under Windows operating system.
  • Fluency with MS Office suite apps (Word, Excel, Power Point).

G. Excellent interpersonal and communication skills (reading, writing, and speaking) in English, including the ability to effectively and efficiently communicate with a multi-cultural team of experts from various technical disciplines and discuss complex subjects with diverse audiences, including via video-conference and online tools.

H. Habitual attention to detail and self-organization skills.

I. Excellent interpersonal skills establishing and maintaining positive and cooperative working relationships across interdisciplinary teams. Ability to work effectively in a shared decision-making environment, by appropriately taking initiative, or seeking guidance, working independently or collaboratively, and helping the team succeed.

Preferred qualifications
  • Record of producing high quality science and peer-reviews publications relevant to the jobs.
  • Demonstrated skills enabling delivery of quality results within reasonable timelines in an agile research environment.
  • Conceptual understanding of ecosystem science, forest disturbance and regeneration processes, ecology, through formal education or work experience.
  • Hands-on experience with Landsat, Sentinel-2 (or similar), especially for vegetation mapping.
  • Deep understanding of physical, mathematical, probabilistic, and statistical foundations of remote sensing observation (with airborne/satellite sensors/cameras), multispectral image formation, and image processing and analysis.
  • Enthusiasm and commitment to expand your knowledge and skills, including openness for pursuing new scientific approaches, methods, and analytical tools
  • Experience with Spectral Mixture Analysis in remote sensing, including endmember selection approaches, and/or other statistical methods used to map vegetation fractional cover.
  • Experience with developing automated algorithms and processing pipelines for image sequences/time-series, preferably applied for ecosystem monitoring, phenological analyses or disturbance detection, land cover change, cloud detection, or similar problems.
  • Evidence of expertise in any of these models/topics: Gaussian mixtures, mixtures of regression models, Bayesian sampling, Optimization techniques (linear, non-linear), data/sensor/algorithm fusion, image cross-normalization/cross-calibration or transfer learning.
  • Understanding of relational databases and geospatial data concepts, vector-raster conversion, geographic coordinate system projections and transformations, and other GIS data structures and concepts.
  • Familiarity with version control (source code management) principles and tools, such as SVN, Git.
  • Other qualifications and skills with demonstrated relevance to the job responsibilities.


Document requirements
  • Curriculum Vitae - Your most recently updated C.V.

  • Cover Letter

  • Statement of Contributions to Diversity, Equity, and Inclusion - Contributions to diversity, equity, and inclusion documented in the application file will be used to evaluate applicants. Visit

  • List of Publications and Presentations

  • A brief Statement of Career/Research Interests and Goals (preferably 1-2 pages)

  • Up to three representative publications in English language.

Reference requirements
  • 2 required (contact information only)
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