Jr. Specialist -Young Lab

The FOCAL Lab is recruiting a bachelor's-level Vegetation Data Scientist to contribute to the lab's work at the intersection of disturbance ecology, vegetation mapping, and vegetation management. The Data Scientist will develop scripted analytical workflows and tools to analyze large vegetation datasets, generally focused on remote sensing-derived geospatial data. A significant portion of the Data Scientist's work will support the Open Forest Observatory, a 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 Data Scientist will also support other data-intensive lab projects related to vegetation ecology.

Strong background in reproducible, scripted (e.g., R- and/or Python-based), open-source data processing and statistical or machine-learning analysis of large datasets is required. Experience with deep learning for computer vision is desired and will aid in development of OFO modules for tree species ID from drone imagery and other imagery interpretation tasks. An ecology background is preferred, but most important is a strong data science background. In addition to analysis and code development, the Data Scientist will be expected to contribute substantially to the preparation of scientific publications and to the dissemination of results via workshops, presentations, and project reports. The Data 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 also with collaborators at other institutions, including universities and federal and state agencies.

Per UCD-APM 330, it is inappropriate to appoint an individual with a Master's or Doctoral degree in the relevant discipline or a baccalaureate degree plus three or more years of experience as a Junior Specialist. Candidates who hold a Masters or Doctoral degree will be disqualified from this search.

I. RESEARCH IN SPECIALIZED AREAS (85% 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. Representative specific duties may include, but are not limited to:

1. Improve upon existing computational methodologies to develop scripted (automated) workflows for processing remote sensing imagery and ancillary information into vegetation datasets, optionally employing deep learning and computer vision models.
2. Calibrate and validate remote vegetation classification and mapping workflows by performing machine learning experiments that involve comparing remote sensing-derived vegetation data to field-based inventory data; document results clearly via reproducible code and supporting documentation.
3. Carefully organize large volumes of remote sensing and field-based data in a robust and well documented manner.
4. Contribute toward the development of infrastructure for a searchable index ("metadatabase") of remote sensing-derived vegetation datasets produced by the lab and contributors.
5. Perform statistical and/or machine learning analyses of vegetation and other environmental datasets to derive inferences into ecological patterns and processes.
6. Employ high-performance computing and data storage platforms including Jetstream2 and CyVerse for large analyses and to deploy tools for internal and third-party use.
7. Package scripted workflows into user-friendly software libraries.
8. Lead, co-lead, or contribute substantially to the preparation of scientific manuscripts and code and data releases.
   B. Review research proposals, journal manuscripts, and publications related to area of expertise.
   C. 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:
9. Publications, data releases, or software/tool releases that acknowledge the Specialist's significant and meaningful contribution to the work.
10. Publications, data releases, or software/tool releases on which the Specialist is an author.
11. 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.
12. Active dissemination of information (beyond the boundaries of the campus) through informal instruction, presentations, or other means stemming from the Specialist's research accomplishments.
13. Other evidence of recognized expertise may include formal documentation of intellectual effort and participation in publishable research activities, data releases, or software/tool releases, authorship on publications/patents, presentation of research at regional/national/international meetings, invitations to review grant proposals and/or manuscripts, invitations to participate in research projects, and/or service on advisory panels.
    D. 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 vegetation 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 (10% EFFORT)

A. Participate in scholarly conferences such as the Ecological Society of America annual meeting and conferences organized by the Computer Vision Foundation by developing materials for presentation and, as funding permits, traveling to present the materials in person.

B. Participate in appropriate professional/technical societies or groups and other educational and research organizations. For example, interface with the University of Arizona CyVerse program, the CU Boulder Earth Lab, and the UC Berkeley AI Research Lab Climate Initiative by attending and participating in their seminars and workshops, and/or by communicating with academics at these institutions to share and discuss solutions to mutual data analysis problems. Develop collaborative projects with and provide support to other research groups working to employ the tools developed by the lab.

C. Participate in the development of tutorials, curricula, and/or other training materials to facilitate

D. In collaboration with the supervisor, develop funding opportunities (including co-leading or contributing to funding proposals) to further the research goals of the lab.

E. Mentor junior lab members in data science tasks.

III. UNIVERSITY AND PUBLIC SERVICE (5% EFFORT)

A. May maintain liaison and respond to the needs of various industry organizations, state and federal agencies, and other external groups on issues related to area of expertise.

B. May participate in activities of committees within the department, college, campus and other University entities, as appropriate. For example, may serve on the departmental IT committee, support public outreach events such as Picnic Day exhibits, assist in coordination of workshops, contribute to public trainings and tutorials on the use of research tools.

A. Bachelor's degree in remote sensing, computer science, data science, or related field (must be obtained at the time of appointment).

B. Proficiency with tabular and geospatial data processing in R and/or Python and via cloud computing.

C. Experience with machine learning and software engineering in both academic and industry contexts, including containerized workflows and collaboration using version control systems.

D. Experience working with very large tabular and geospatial vector and raster datasets.

E. Evidence of ability to contribute to research published in peer-reviewed journals.

F. Understanding of and interest in vegetation ecology and disturbance ecology concepts

G. Experience with statistical modeling for hypothesis testing

H. Experience developing interactive websites and web applications

• 2 required (contact information only)

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