Job DescriptionJob Description The Bay Area Environmental Research (BAER) Institute, a 501(c)(3) nonprofit organization focused on enabling and conducting research in Earth and space science, is seeking two Remote Sensing Data Scientists for the NASA Ames Earth Science Division with experience in the areas of remote sensing and advanced data analytics including machine learning. These scientists will participate in developing remote-sensing-based landscape change monitoring products and associated processing pipelines. The landscape change monitoring products relevant to this hire include (a) land cover mapping with data fusion, and (b) change detection and attribution products. The positions will work with the NASA Earth Exchange (NEX; at NASA Ames Research Center. NEX combines state-of-the-art supercomputing, Earth system modeling, and NASA remote sensing data feeds to deliver a work environment for exploring and analyzing terabyte- to petabyte-scale datasets covering large regions. The positions are located at NASAās Ames Research Center (ARC), Moffett Field, CA, and are initially for 2 years and 9 months with the possibility of extension. Telework and remote work may be available and must comport with the position requirements. Expected start date is spring 2024. Essential Job Duties Develop landscape change monitoring products; Work with the project team on data collection, algorithm development and evaluation, processing pipeline development and delivery; Prepare project progress and final reports, and present them to partners; Develop documentation and training materials to allow team members and partners to successfully operate the data processing pipeline; Prepare and submit research manuscripts to peer-reviewed journals; Present research findings at conferences and public forums; Assist in the development of grant proposals for project sustainability; Contribute to the research community in Ames Earth Science division and beyond; Minimum/ Required Qualifications Ph.D. (or Masterās Degree with more than 3-years of experience in this field) in remote sensing, computer science, environmental science and engineering, or a relevant multidisciplinary degree with experience in land system science, forest ecology, and geography; Strong programming skills in a scientific language (e.g., Python, Matlab, R) and experience in statistical analysis; Demonstrated experience in planning, designing, executing, and coordinating research; Demonstrated proficiency for independently writing scientific publications for submission to peer-āreviewed journals; Experience developing and maintaining appropriate documentation and training materials to allow team members and customers of various backgrounds and disciplines to successfully operate; Strong communication skills that allows for working effectively as a member of a research team; Preferred Qualifications Previous research with remote sensing based terrestrial ecosystem science, (e.g., land use/cover change); Knowledge and experience with quantitative approaches in terrestrial ecosystem & land system science; Leadāauthor on publications for remote sensing application for terrestrial ecosystem science (specifically, land use/cover change mapping); Familiarity with optical image processing, multi-sensor data processing for the purposes of data analytics; Familiarity with high performance computing (HPC) environments; Experience with git is a plus; To be considered for this position Please submit a cover letter (usually one to two pages) to āā with subject line āNEX-WERC Cluster 1: your nameā stating the applicant’s interest in the project, prior research experience and/or projects of particular relevance, areas of interest, key competencies, and anticipated date of availability. Include also a full CV and the names of three references. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age or protected veteran status. Review of applications will begin on January 15th, 2024, and continue until the position is filled. Company DescriptionABOUT BAY AREA ENVIRONMENTAL RESEARCH INSTITUTE Bay Area Environmental Research (BAER) Institute is a growing, non-profit organization dedicated to promoting and conducting research in science, particularly atmospheric and space science. Since its establishment in 1993, BAER has become well recognized nationally for its significant contributions to various fields of scientific research. BAERās clientele include various federal government agencies, and BAER has had a long-standing collaborative relationship with NASA. The Instituteās research and program leadership activities with NASA include work with both the Earth Science and Space Science Divisions at NASA Ames Research Center in Mountain View. BAER has developed and maintained an outstanding reputation for its project management, financial reporting, and program and partnership development capabilities as well as for its resourcefulness in providing expert scientists, researchers, and support staff for mission-critical Agency projects.Company DescriptionABOUT BAY AREA ENVIRONMENTAL RESEARCH INSTITUTE\r\nBay Area Environmental Research (BAER) Institute is a growing, non-profit organization dedicated to promoting and conducting research in science, particularly atmospheric and space science. Since its establishment in 1993, BAER has become well recognized nationally for its significant contributions to various fields of scientific research. BAERās clientele include various federal government agencies, and BAER has had a long-standing collaborative relationship with NASA. The Instituteās research and program leadership activities with NASA include work with both the Earth Science and Space Science Divisions at NASA Ames Research Center in Mountain View. BAER has developed and maintained an outstanding reputation for its project management, financial reporting, and program and partnership development capabilities as well as for its resourcefulness in providing expert scientists, researchers, and support staff for mission-critical Agency projects.
Git statistics Data Science Machine Learning data-processing Cloud Data Fusion attribution