Postdoctoral Research Associate - Machine Learning for Human Dynamics
Overview:
The National Security Sciences Directorate at Oak Ridge National Laboratory leads scientific and technological breakthroughs to confront some of the nation’s most difficult security challenges. We develop interdisciplinary applications needed for the security of our nation today and target our vision on how these challenges may manifest themselves in a decade or more.
Our research and development focuses on cybersecurity and cyber physical resiliency, data analytics, geospatial science and technology, nuclear nonproliferation, and high-performance computing for sensitive national security missions. We also enhance ORNL contributions to national security challenges by working closely with leading researchers at the lab in areas such as nuclear and chemical sciences and engineering, applied materials, advanced manufacturing, biosecurity, transportation, and computing.
We are currently seeking a qualified applicant for a Postdoctoral Scientist position in Machine Learning for Human Dynamics within the Geographic Data Science Section and GeoAI group. The position requires strong skills in computer science, statistics, mathematics, machine learning, geography, and remote sensing.
The position affords the unique opportunity to work with a talented interdisciplinary team of R&D professionals across research groups to build new research directions. A successful candidate will work on advancing GeoAI methods toward the understanding of human processes as they interact with the virtual, natural, and built environment. This position focuses on machine learning for human dynamics research; specifically, how people occupy space in the built environment at multiple scales. You will use applied statistics and machine learning to model the relationship between built environment characteristics and human population densities across space and time, using statistical data from censuses or other enumerations in conjunction with remotely sensed observations and characterizations of the built environment. Analysis and modeling of sociocultural and demographic factors that influence spatial and temporal population dynamics will also be a focus.
Within the GeoAI group, you will be part of a research team that is developing novel methods for applied machine learning in large scale space-time analytics and multi-modal data fusion, take advantage of the world’s most robust high-performance computing platforms, aim for big geospatial science opportunities to support critical national security missions.
Major Duties and Responsibilities:
- Contribute to and lead the design, development, and implementation of new models, methods, and algorithms that improve our understanding of human dynamics.
- Evaluate and improve existing population modeling processes, workflows, and codebases where appropriate.
- Provide coding support to implement novel uncertainty estimation algorithms as proof of concept or prototype to test effectiveness and robustness of applied machine learning algorithms.
- Support the design of statistical sampling strategies and accelerating the implementation of such tools.
- Contribute to and lead team publications in journals, participate in conferences, and engage with other scientists and analysts in the private sector, academia, and US Government communities.
Basic Qualifications:
- Ph.D. in computational data sciences, geography, statistics, computer engineering, remote sensing or equivalent field completed within the last 5 years
- 1-2 years of research and development experience.
- Demonstrated written and oral scientific communication skills including peer-reviewed publications and presentations.
- Demonstrated record of research as evidenced by scientific output including peer-reviewed publications.
- Excellent skills in written and verbal communication, as well as teamwork.
- Experience working collaboratively in version control systems for source code management such as Git/GitLab.
- Experience with Python, R, and/or other similar languages for statistical analysis.
- Experience with Pandas, Numpy, Pytorch, and other similar tools for machine and deep learning.
- Experience with database technologies, particularly to store, analyze, and manipulate geospatial data.
Preferred Qualifications:
- Knowledge of machine learning and computational approaches for geospatial modeling across multiple spatiotemporal scales.
- Knowledge in geospatial modeling and analysis including use of open source and/or commercial geospatial tools such as ArcGIS, QGIS, PostGIS.
- Experience with remote sensing and image interpretation including multispectral and hyperspectral data, spectral analysis, and LiDAR.
- Experience with parallel processing on CPU and/or GPU.
- Experience with HPC and workload managers such as Slurm or PBS.
- Experience leading research initiatives in a team environment.
- Experience in analyzing and interacting with social media, digital trace data, or time series data.
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 for up to 24 months with the potential for extension. Initial appointments and extensions are subject to performance and the availability of funding.
Moving can be overwhelming and expensive. UT-Battelle offers a generous relocation package to ease the transition process. Domestic and international relocation assistance is available for certain positions. If invited to interview, be sure to ask your Recruiter (Talent Acquisition Partner) for details.
For more information about our benefits, working here, and living here, visit the “About” tab at jobs.ornl.gov.
This position will remain open for a minimum of 5 days after which it will close when a qualified candidate is identified and/or hired.
We accept Word (.doc, .docx), Adobe (unsecured .pdf), Rich Text Format (.rtf), and HTML (.htm, .html) up to 5MB in size. Resumes from third party vendors will not be accepted; these resumes will be deleted and the candidates submitted will not be considered for employment.
If you have trouble applying for a position, please email ORNLRecruiting@ornl.gov.
ORNL is an equal opportunity employer. All qualified applicants, including individuals with disabilities and protected veterans, are encouraged to apply. UT-Battelle is an E-Verify employer.