Jobs:Job-01277
Apply before: 7 August 2023
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Posting:
Position: Postdoctoral Position
Apply before: 7 August 2023
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Project Description: Prof. Yufang Jin (https://jin.ucdavis.edu/) and Prof. Greg Pasternack (https://pasternack.ucdavis.edu/) in the Department of Land, Air, and Water Resources at the University of California Davis invite applications for a postdoctoral scholar position to research urban stream corridors (USCs), addressing intertwined ecological, hydrological, geomorphic, hydraulic, and socio-economic issues as they exist today and forecasting for 2050 under climate change. Urban areas in California span a wide range of “genetic” environmental conditions, yielding a variety of USC archetypes. In turn, USC archetypes have different physical hazards, support unique habitats and ecological functions, and present different opportunities and challenges for unhoused folks and their social support services. This is the hazard-habitat-unhoused (HHU) nexus. However, USC archetypes are poorly known, classified, or spatially mapped, limiting our understanding of the present HHU nexus and forecasting how they will change in the future. We are seeking a highly motivated scholar to develop an AI model predicting the contemporary spatial pattern of USC types in California’s geographically complex Bay Area, and then forecast its future changes in 2050 under climate change and economic development, to assess risks and opportunities. This position will be 1 of 3 postdocs involved, with the others addressing (i) socio-ecology of USC unhoused folks and (ii) USC eco-geomorphology and ecohydraulics. Applicants may also indicate their interest in one of the other open positions if that is as good or better for the applicant’s scholarship.
Job Duties: This position will involve four major activities building on existing code and methods with novel developments based on the applicant’s creativity and scholarship. First, use remote sensing and informatics to obtain and curate the critical regional datasets characterizing current USC physical, ecological, and socio-economic conditions. Second, perform AI modeling to predict the spatial stream network pattern of USC types throughout the Bay Area region using unique training data collected by other team members. Third, obtain and curate existing datasets predicting regional conditions in 2050, drawing on available forecasts for climate (dynamically and statistically downscaled), flood hydrology, land use, human population, and other available forecasts for 2050. Finally, run trained AI models to forecast USC types and associated hazard-habitat-unhoused conditions in 2050.
Given the interdisciplinary nature of the research, other professional skills and experiences that could be beneficial for hiring might include a background in or demonstrated experience with computational algorithms (e.g., image processing, machine learning, and optimization), parallel computing, GPU-based numerical modeling, experience with API’s for different remote sensing data sources and products, experience with neural network and deep learning modeling, climate modeling, hydrologic modeling, hydraulic modeling, and ecological function analysis, desire & proficiency to publish in scientific journals, ability to communicate (oral and in print) research findings to multiple academic and nonacademic audiences, and/or transdisciplinary outreach with underserved urban communities. Hands on experience with operating drones and working with point cloud datasets such as aerial lidar are a plus.
Job Qualifications & Expectations: Applicants must possess a PhD from an accredited university by September 1, 2023 in one of the following fields: Remote Sensing, Computer Science, Data Science, Geography, Ecology, Civil (aka Infrastructural, Environmental, or Hydraulic) Engineering, Hydrology, Geology, Atmospheric Science, Urban Studies, Applied Math, Statistics, or related field. The scholar will be expected to adhere to project goals and tasks, while taking initiative with duties. The scholar will be expected to collaborate compassionately, inclusively, and effectively among diverse peers, students, and as part of the larger team from academia, local government, and nonprofits, spanning diverse disciplines. Must have remote sensing and informatics experience. Must have strong analytical and quantitative skills, with experience in spatial and temporal data analysis and modeling. Must be proficient in coding in either R or Python. Must have experience with machine learning or other forms of artificial intelligence. Must be proficient in written and spoken English. Remotely located employment is not an option. Demonstrated experience and skill with organization, time management, data management, oral and written communication, and scientific publication are preferred. The successful candidate will receive training as needed to supplement their initial capabilities.
Salary & Benefits: The initial appointment duration for this 100% time position is two years. All University of California postdoctoral scholars receive benefits and are paid according to the same union-negotiated salary scale with mandatory annual increases. The successful applicant’s salary will be determined based on their individual status with respect to “step” criteria at the time their employment begins.
Application & Contract Information: Applications are open for submission now and they will be reviewed on a rolling basis. Applications received before August 7, 2023 are guaranteed to be given full consideration. Candidates should send a well-crafted cover letter summarizing their research and career interests tying to their reason for applying for this job, a curriculum vitae, and contact information for three academic or professional references. All applications (e-mailed as a single PDF file) should be emailed to Professor Greg Pasternack <gpast@ucdavis.edu>.
University Information: This position is based in the. UC Davis is a smoke and tobacco-free campus (http://breathefree.ucdavis.edu/). We are an Affirmative Action/Equal Opportunity employer, and particularly encourage applications from members of historically underrepresented racial/ethnic groups, women, individuals’ with disabilities, veterans, LGBTQ community members, and others who demonstrate the ability to help us achieve our vision of a diverse and inclusive community. For the complete University of California nondiscrimination and affirmative action policy see: http://policy.ucop.edu/doc/4000376/NondiscrimAffirmAct
Under Federal law, the University of California may employ only individuals who are legally able to work in the United States as established by providing documents as specified in the Immigration Reform and Control Act of 1986. Certain UC Davis positions funded by federal contracts or sub-contracts require the selected candidate to pass an E-Verify check. More information is available at: http://www.uscis.gov/e-verify.
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As a condition of employment, you will be required to comply with the University of California Policy on Vaccination Programs - With Updated Interim Amendments. All Covered Individuals under the policy must provide proof of receiving the COVID-19 Vaccine Primary Series or, if applicable, submit a request for Exception (based on Medical Exemption, Disability, Religious Objection, and/or Deferral based on pregnancy or recent COVID-19 diagnosis and/or treatment) no later than the applicable deadline. All Covered Individuals must also provide proof of receiving the most recent CDC-recommended COVID-19 booster or properly decline such booster no later than the applicable deadline. New University of California employees should refer to Exhibit 2, Section II.C. of the SARS-CoV-2 (COVID-19) Vaccination Program Attachment for applicable deadlines. All Covered Individuals must also provide proof of being Up-To-Date on seasonal influenza vaccination or properly decline such vaccination no later than the applicable deadline. Please refer to the Seasonal Influenza Vaccination Program Attachment. (Capitalized terms in this paragraph are defined in the policy.) Federal, state, or local public health directives may impose additional requirements.
