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<p class="MsoNormal"><b><span style="font-size:11.0pt">Postdoctoral Research Associate - AI/ML for Gulf Coast Ecosystem Dynamics (Oak Ridge National Lab)<o:p></o:p></span></b></p>
<p class="MsoNormal"><b><span style="font-size:11.0pt"><a href="https://jobs.ornl.gov/job/Oak-Ridge-Postdoctoral-Research-Associate-AIML-for-Gulf-Coast-Ecosystem-Dynamics-TN-37830/1427761600/">https://jobs.ornl.gov/job/Oak-Ridge-Postdoctoral-Research-Associate-AIML-for-Gulf-Coast-Ecosystem-Dynamics-TN-37830/1427761600/</a><o:p></o:p></span></b></p>
<p class="MsoNormal"><b><span style="font-size:11.0pt"><o:p> </o:p></span></b></p>
<p class="MsoNormal"><b><span style="font-size:11.0pt">Overview:</span></b><span style="font-size:11.0pt"><o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt">The Computational Hydrology and Atmospheric Science (CHAS) Group within the Computational Sciences and Engineering Division (CSED) at Oak Ridge National Laboratory (ORNL) is seeking a highly motivated Postdoctoral
Researcher with expertise in artificial intelligence and machine learning (AI/ML), remote sensing, Earth and environmental sciences, and the analysis of large-scale geospatial and time-series datasets. The candidate will develop and evaluate multimodal AI
models to characterize vegetation and land-surface dynamics and quantify ecosystem responses and recovery following hurricanes and other disturbances.<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt"> <o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt">The successful candidate will directly support the Exploring Gulf Region Ecosystem Transitions (EGRET) project, an interdisciplinary and multi-institutional collaboration focused on disturbance-driven ecosystem
transitions and their impacts across the United States Gulf Coast. EGRET employs an integrated model–experiment (ModEx) approach accelerated by AI to advance predictive understanding of how plant–microbial–soil interactions vary across inundation and salinity
gradients to shape ecological, hydrological, and geomorphological responses to abrupt disturbance at scale. A cohort of postdoctoral researchers will be hired across multiple institutions to collaboratively support scientific advances guided by AI/ML, remote
sensing, process-based modeling, field observations and experiments, and advanced analytical techniques.<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt"> <o:p></o:p></span></p>
<p class="MsoNormal"><b><span style="font-size:11.0pt">Major Duties/Responsibilities:</span></b><span style="font-size:11.0pt"><o:p></o:p></span></p>
<ul style="margin-top:0in" type="disc">
<li class="MsoNormal" style="mso-list:l1 level1 lfo1"><span style="font-size:11.0pt">Develop and apply AI/ML methods to integrate heterogeneous geospatial, remote-sensing, hydrological, meteorological, and environmental datasets across the Gulf Coast.<o:p></o:p></span></li><li class="MsoNormal" style="mso-list:l1 level1 lfo1"><span style="font-size:11.0pt">Intergrade remote sensing approaches and AI models to characterize vegetation and land-surface dynamics and quantify ecosystem responses and recovery following hurricanes and
other disturbances at scale.<o:p></o:p></span></li><li class="MsoNormal" style="mso-list:l1 level1 lfo1"><span style="font-size:11.0pt">Apply explainable AI and statistical methods to identify physical, environmental, and biological drivers of ecosystem change and resilience, quantify nonlinear interactions,
and advance scientific understanding of coastal ecosystem resilience.<o:p></o:p></span></li><li class="MsoNormal" style="mso-list:l1 level1 lfo1"><span style="font-size:11.0pt">Curate and develop reproducible AI-ready datasets, computational workflows, and research software for collaborative use within the EGRET project.<o:p></o:p></span></li><li class="MsoNormal" style="mso-list:l1 level1 lfo1"><span style="font-size:11.0pt">Work closely with remote-sensing scientists, hydrologists, environmental scientists, process-based modelers, and computational scientists across DOE laboratories and universities
to address project objectives.<o:p></o:p></span></li><li class="MsoNormal" style="mso-list:l1 level1 lfo1"><span style="font-size:11.0pt">Present research results within the project and at national and international conferences, and publish findings in peer-reviewed journals, and datasets in DOE data repositories.<o:p></o:p></span></li><li class="MsoNormal" style="mso-list:l1 level1 lfo1"><span style="font-size:11.0pt">Deliver ORNL’s mission by aligning behaviors, priorities, and interactions with our core values of Impact, Integrity, Teamwork, Safety, and Service.<o:p></o:p></span></li></ul>
<p class="MsoNormal"><span style="font-size:11.0pt"> <o:p></o:p></span></p>
<p class="MsoNormal"><b><span style="font-size:11.0pt">Basic Qualifications:</span></b><span style="font-size:11.0pt"><o:p></o:p></span></p>
<ul style="margin-top:0in" type="disc">
<li class="MsoNormal" style="mso-list:l3 level1 lfo2"><span style="font-size:11.0pt">A Ph.D. in Earth and Environmental Sciences, Hydrology, Computational Sciences, Remote Sensing, Data Science, or a related field, completed within the last 5 years or expected
to be completed soon.<o:p></o:p></span></li><li class="MsoNormal" style="mso-list:l3 level1 lfo2"><span style="font-size:11.0pt">Demonstrated experience developing or applying AI/ML approaches to Earth, environmental, ecological, hydrological, or geospatial problems.<o:p></o:p></span></li><li class="MsoNormal" style="mso-list:l3 level1 lfo2"><span style="font-size:11.0pt">Experience with programming in Python, GEE, or R, and working with modern scientific computing and/or AI/ML frameworks.<o:p></o:p></span></li><li class="MsoNormal" style="mso-list:l3 level1 lfo2"><span style="font-size:11.0pt">Experience with analyzing large, heterogeneous environmental, geospatial, remote-sensing, or time-series datasets.<o:p></o:p></span></li><li class="MsoNormal" style="mso-list:l3 level1 lfo2"><span style="font-size:11.0pt">A strong record of scholarly productivity, as demonstrated by peer-reviewed publications and/or presentations at scientific conferences.<o:p></o:p></span></li><li class="MsoNormal" style="mso-list:l3 level1 lfo2"><span style="font-size:11.0pt">Excellent written and oral communication skills and the ability to work effectively in a collaborative, multidisciplinary team environment.<o:p></o:p></span></li></ul>
<p class="MsoNormal"><span style="font-size:11.0pt"> <o:p></o:p></span></p>
<p class="MsoNormal"><b><span style="font-size:11.0pt">Preferred Qualifications:</span></b><span style="font-size:11.0pt"><o:p></o:p></span></p>
<ul style="margin-top:0in" type="disc">
<li class="MsoNormal" style="mso-list:l0 level1 lfo3"><span style="font-size:11.0pt">Experience with AI/ML approaches for spatiotemporal or Earth system data, including transformers, multimodal learning, representation learning, or related architectures.<o:p></o:p></span></li><li class="MsoNormal" style="mso-list:l0 level1 lfo3"><span style="font-size:11.0pt">Experience working with satellite remote-sensing data such as Landsat, Sentinel, MODIS, SAR, LiDAR, or derived land-cover and vegetation product, and experience with in-the-cloud
image processing<o:p></o:p></span></li><li class="MsoNormal" style="mso-list:l0 level1 lfo3"><span style="font-size:11.0pt">Experience with geospatial foundation models or pretrained Earth-observation models.<o:p></o:p></span></li><li class="MsoNormal" style="mso-list:l0 level1 lfo3"><span style="font-size:11.0pt">Experience with explainable AI, feature attribution, dimensionality reduction, clustering, representation analysis, or related approaches for extracting scientific understanding
from AI/ML models.<o:p></o:p></span></li><li class="MsoNormal" style="mso-list:l0 level1 lfo3"><span style="font-size:11.0pt">Demonstrated experience conducting interdisciplinary, systems-level research that integrates AI/ML with Earth and environmental science.<o:p></o:p></span></li><li class="MsoNormal" style="mso-list:l0 level1 lfo3"><span style="font-size:11.0pt">Experience with coastal, wetland, or ecosystem ecology is preferred<o:p></o:p></span></li></ul>
<p class="MsoNormal"><span style="font-size:11.0pt"> <o:p></o:p></span></p>
<p class="MsoNormal"><b><span style="font-size:11.0pt">Special Requirements:</span></b><span style="font-size:11.0pt"><o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt"> <o:p></o:p></span></p>
<p class="MsoNormal"><b><span style="font-size:11.0pt">Applicants cannot have received their Ph.D. more than five years prior to the date of application and must complete all degree requirements before starting their appointment. The appointment length will
be up to 24 months with the potential for extension. Initial appointments and extensions are subject to performance and availability of funding.</span></b><span style="font-size:11.0pt"><o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt"> <o:p></o:p></span></p>
<p class="MsoNormal"><b><span style="font-size:11.0pt">Letters of Reference</span></b><span style="font-size:11.0pt">: Please submit three letters of reference when applying to this position. You may upload these directly to your application or have them sent
to<o:p></o:p></span></p>
<ul style="margin-top:0in" type="disc">
<li class="MsoNormal" style="mso-list:l2 level1 lfo4"><span style="font-size:11.0pt">Instructions to upload documents to your candidate profile:<o:p></o:p></span></li><li class="MsoNormal" style="mso-list:l2 level1 lfo4"><span style="font-size:11.0pt">Login to your account via jobs.ornl.gov<o:p></o:p></span></li><li class="MsoNormal" style="mso-list:l2 level1 lfo4"><span style="font-size:11.0pt">View Profile<o:p></o:p></span></li><li class="MsoNormal" style="mso-list:l2 level1 lfo4"><span style="font-size:11.0pt">Under the My Documents section, select Add a Document<o:p></o:p></span></li></ul>
<p class="MsoNormal"><span style="font-size:11.0pt"> <o:p></o:p></span></p>
<p class="MsoNormal"><b><span style="font-size:11.0pt">Technical Questions:</span></b><span style="font-size:11.0pt"><o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt">Please contact Dan Lu: lud1@ornl.gov; Daryl Yang: yangd@ornl.gov<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt"> <o:p></o:p></span></p>
<p class="MsoNormal"><b><span style="font-size:11.0pt">Project Questions:</span></b><span style="font-size:11.0pt"><o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt">Please contact Elizabeth Herndon: herndonem@ornl.gov; Benjamin Sulman: sulmanbn@ornl.gov<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt"><o:p> </o:p></span></p>
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