The FOCAL Lab is recruiting a Spatial Data Scientist to contribute to the lab's work at the intersection of disturbance ecology, forest mapping, and forest management. The Scientist will develop scripted analytical workflows and tools to analyze large geospatial datasets. A large amount of the Scientist's work initially will support the Open Forest Observatory, a new NSF-funded project on the frontier of drone-enabled forest ecology. The OFO is building open-source tools to (a) map forests at the individual-tree level using low-cost drone technology and (b) make the resulting maps publicly available. The Scientist will also lead or co-lead other data-intensive lab projects, including a project to classify vegetation cover and quantify changes over time at UC Natural Reserves using repeat collections of drone imagery. The position will also support a collaboration with USGS that aims to understand fine-scale variation in tree mortality in areas of recent large wildfires in California by analyzing high-resolution imagery (drone, airplane, and satellite) and plot-based field data.

Strong background in reproducible, scripted (e.g., R- and/or Python-based), open-source geospatial processing and machine-learning analysis of large datasets is required. Experience with deep learning for computer vision is required and will aid in development of OFO modules for tree species ID from drone imagery and other imagery interpretation tasks. Background in forest vegetation data analysis and drone-based imagery collection and analysis is also required. In addition to analysis and code development, the Scientist will be expected to lead, co-lead, or contribute substantially to the preparation of scientific publications and to the dissemination of results via workshops, presentations, and project reports. The Scientist will also collaborate with the supervisor to develop funding opportunities to further the research goals of the FOCAL Lab. The Scientist will report to FOCAL Lab PI Derek Young and will work closely with collaborators at UC Davis, CU Boulder, U of Arizona, CyVerse, and other institutions.

Major responsibilities and designated areas of expertise

I. Research and software/tool development in specialized areas (80% effort)
A. Collaborate with other research and extension personnel affiliated with research activities involving vegetation ecology, disturbance ecology, vegetation management, vegetation mapping, remote sensing, computer vision, machine learning, research infrastructure/software development, and/or other spatial data science topics. Specific duties include:

1. Improve upon existing methodologies to develop scripted (automated) workflows for processing drone imagery into individual tree-based forest maps, optionally employing deep learning computer vision models.
2. Calibrate and validate forest mapping workflows by comparing drone-derived forest maps to extensive field-based forest inventory data, including NEON data.
3. Develop the infrastructure for a searchable index (“metadatabase”) of drone-derived forest maps produced by the OFO and contributors.
4. Employ high-performance computing and data storage platforms including Jetstream2 and CyVerse for large analyses and to deploy tools for third-party use.
5. Package scripted workflows into user-friendly software libraries.
6. Lead, co-lead, or contribute substantially to the preparation of scientific manuscripts and code and data releases.

Evaluation of performance in research activities or in outreach activities as deemed applicable to the individual project in specialized areas, as documented by any of the following:

1. Publications, data releases, or software/tool releases that acknowledge the Specialist's significant and meaningful contribution to the work.
2. Publications, data releases, or software/tool releases on which the Specialist is an author.
3. Other evidence (e.g., letters from collaborators or principal investigators) that work done by the Specialist contributed to publishable research, data releases, or software/tool releases.
4. Active dissemination of information (beyond the boundaries of the campus) through informal instruction, presentations, or other means stemming from the Specialist's research accomplishments.
5. Other evidence of recognized expertise may include formal documentation of intellectual effort and participation in publishable research activities, data releases, or software/tool releases, first authorship on publications/patents, presentation of research at regional/national meetings, invitations to review grant proposals and/or journal articles, invitations to participate in research projects, and/or service on advisory panels. B. Lead and/or coordinate specific components of broader projects involving vegetation ecology, disturbance ecology, vegetation management, vegetation mapping, remote sensing, computer vision, machine learning, research infrastructure/software development, and/or other spatial data science topics. C. Serve as a research coordinator for vegetation ecology, disturbance ecology, vegetation management, vegetation mapping, remote sensing, computer vision, machine learning, research infrastructure/software development, and/or other spatial data science projects conducted by PIs in the department, as resources permit. Responsible for ensuring communication between project contributors, managing data and code, and preparing reports tailored to meet the needs of the requesting individual(s).

II. Professional competence and activity (20% effort)
A. Participate in appropriate professional/technical societies or groups and other educational and research organizations.
B. Review research proposals, journal manuscripts, and publications related to area of expertise.
C. In collaboration with the supervisor, develop funding opportunities (including co-leading or contributing to funding proposals) to further the research goals of the lab.
D. Mentor junior lab members in data science tasks.

Per UCD-APM 330, it is inappropriate to appoint an individual with a Doctoral degree in the relevant discipline as an Assistant Specialist.

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