Requisition Id 13320
Overview:
As a U.S. Department of Energy (DOE) Office of Science national laboratory, Oak Ridge National Laboratory (ORNL) has an extraordinary 80-year history of solving the nation’s biggest problems. We have a dedicated and creative staff of over 6,000 people! Our vision for diversity, equity, inclusion, and accessibility (DEIA) is to cultivate an environment and practices that foster diversity in ideas and in the people across the organization, as well as to ensure ORNL is recognized as a workplace of choice. These elements are critical for enabling the execution of ORNL’s broader mission to accelerate scientific discoveries and their translation into energy, environment, and security solutions for the nation.
We are seeking a Geospatial Scientist to support ORNL’s growing Climate Security research portfolio. This position resides within the Spatial Statistics Group of the Geospatial Science and Human Security Division at ORNL.
As part of our team, you will support and lead research activities related to the current and emerging impacts of climate change on populations, communities, critical infrastructure, and supply chains. Using your knowledge and expertise in spatial statistics, spatiotemporal analytics, remote sensing, and data science, you will perform research and development for US government sponsors that helps the national security community better understand and mitigate environmental risk. Particular focus areas will include support to water security risk modeling, land cover change modeling, and characterizing coastal critical infrastructure risk from sea-level rise and subsidence.
The Spatial Statistics Group is within ORNL’s Geographic Data Science Section and focuses on developing and applying advanced statistical and mathematical solutions at scale to address complex and interdisciplinary problems affecting national security. There is a strong emphasis on advancing novel capabilities and systems within operational environments that leverage high performance computing (HPC) and automated workflows. Publishing results in top tier journals and conferences is a high priority.
Major Duties/Responsibilities:
• Lead R&D tasks applying spatial and spatiotemporal analytics, including advanced methods from probability modeling, spatial and temporal statistics, Bayesian reasoning, etc.
• Perform R&D on climate change risks and impacts leveraging diverse geospatial and scientific datasets, including global remote sensing datasets, terrestrial and in situ data, and modeled data.
• Contribute innovative science in spatial analytics, with emphasis on developing workflows in HPC environments.
• Make impacts through novel S&T delivered to sponsors, successful proposals, S&T presentations, professional community engagement, inventions/patents/copyrights, peer-reviewed publications, etc.
• All team members deliver ORNL’s mission by aligning behaviors, priorities, and interactions with our core values of Impact, Integrity, Teamwork, Safety, and Service. Promote diversity, equity, inclusion, and accessibility by fostering a respectful workplace – in how we treat one another, work together, and measure success.
Basic Qualifications:
• MS in geospatial science, Earth systems science, applied statistics, mathematics, computer science, or related field and two years of relevant experience.
• Demonstrated experience working with spatial and spatiotemporal data, including structured and unstructured datasets, remote sensing datasets, time series data, etc.
• Demonstrated experience applying spatiotemporal methods to address environmental, infrastructure, or human risk research questions.
• Experience presenting scientific results to sponsors and technical communities, as well as at professional society conferences and workshops.
Preferred Qualifications:
• PhD in geospatial science, Earth systems science, applied statistics, mathematics, computer science, or related field.
• General familiarity with climate change research and potential future risk scenarios.
• Experience with climate and Earth systems models, either developing inputs or using outputs.
• Experience characterizing scenario-specific risk using data-driven approaches.
• Experience combining multiple sources of scientific data to address research questions, with knowledge of how to manage and mitigate effects of disparate scales, accuracy, and uncertainty.
• Experience working at large spatial scales (country-scale or larger).
• Experience developing or applying machine learning models for R&D.
• Excellent interpersonal skills and a strong commitment to a team environment.
• Strong written and oral communication skills.
• Ability to function well in a fast-paced research environment, set priorities to accomplish multiple tasks within deadlines, and adapt to changing needs.
• Motivated self-starter with the ability to work independently and to participate creatively in collaborative teams across the laboratory.
Special Req
Location: Oak Ridge, TN
Posted: Sept. 2, 2024, 8:05 a.m.
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