Research Scientist, Spatial Statistics

Requisition Id 13722 

Overview:  

As a U.S. Department of Energy (DOE) Office of Science 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 searching for a Research Scientist to join the newly formed Spatial Statistics group. This group focuses on developing and applying advanced statistical and mathematical solutions at scale to complex and interdisciplinary problems affecting national security. There is a strong emphasis on advancing novel capabilities and systems within operational environments that require high performance computing (HPC) and automated workflows.

 

Application domains include population dynamics, climate security, public and environmental health, sociocultural/economic trend analysis, built environment attribution, secure transportation, nuclear nonproliferation, change detection, and formal protections against privacy attacks and other forms of geoassurance. Methods vary widely but include applied mathematics, deep learning (DL), machine learning (ML), probability modeling, Bayesian reasoning, quantum computing, spatial statistics, spatio-temporal modeling, decision support, and data mining. Problem sets are often driven by “big data” environments, but many applications must contend with sparse or noisy data and creatively turn to multi-modal inference linkages, subject matter elicitation, and other methods for estimating key endpoints under uncertainty. In many cases, the groups’ capabilities support situational awareness and decision-making processes under uncertainty in real operational settings. The ability to envision, develop, and effectively communicate complex analytical methods to non-technical collaborators is a key asset.

 

As a Research Scientist, you will have the opportunity and the expectation to engage directly in your own research, to publish in peer review journals and conferences, to serve within professional societies, to develop your professional profile and network nationally and internationally. You will help coordinate research efforts across the group and ensure that efforts are well aligned with the needs of customers while advancing the state of the art.

 

Major Duties/Responsibilities: 

• Think innovatively about the science of spatial statistics with emphasis on workflows in high-performance computing environments.
• Initiate, lead, and perform independent and impactful R&D on an ongoing basis as evidenced by, for example, innovative S&T delivered to sponsors, successful proposals, S&T presentations, professional community engagement, inventions/patents/copyrights, publications as appropriate, 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:

• PhD in applied statistics, mathematics, computer science, or related field and two (2) years' relevant experience. An equivalent combination of education and experience may be considered.

• Demonstrated experience working with spatial and spatiotemporal data including structured and unstructured, remote sensing, text, time series analysis etc.
• Experience in two or more areas of applied statistics, spatial statistics, probabilistic modeling (e.g,, Bayesian), spatio-temporal methods, and ML/DL, etc.
• Experience developing proof of concept code in R, Python, etc.

 

 Preferred Qualifications: