Research Engineer Must be able to work onsite 3-4 days a week in Oak Ridge, TN. Must be eligible for a federal Security Clearance (US Citizen)

Overview: As a Research Engineer, you will contribute to cutting-edge research and development efforts aimed at enhancing the resilience of critical infrastructure systems against various threats and disruptions. This position offers a unique opportunity to work on interdisciplinary projects in collaboration with leading experts, tackling challenges related to energy (all sources including solar/wind/gas/microgrids/battery storage, etc.), water, cybersecurity, transportation, and more, with a special emphasis on grid reliability and resilience assessments and equity analysis. You will have the opportunity to creatively use interdisciplinary methods from computational data science, machine learning/GenAI, geographical information sciences, and many other methodologies to help frame and solve the above problems on a national and global scale. The successful candidate will contribute to cutting-edge research and development efforts aimed at enhancing the resilience of critical infrastructure systems against various threats and disruptions. You will work on advancing analytic methods and tools to improve understanding of critical infrastructure resilience, specifically improving access to electrical power for climate resilience and grid security. Your research will help to inform investments in grid resilience and restoration, deliver immediate benefits to our existing emergency response systems, and provide visibility into disparities in access to affordable energy. Major Duties/Responsibilities: Contribute to the design, development, and implementation of new models, methods, and algorithms that improve our understanding of critical infrastructure resilience, specifically improving access to electrical power for climate resilience and grid security purposes. Conduct research on grid resilience assessment methodologies, including modeling and simulation of grid operations under stress conditions, identification of vulnerabilities, and development of resilience enhancement strategies. Analyze large-scale datasets from diverse sources to extract insights relevant to grid resilience. Apply statistical techniques and machine learning algorithms to identify patterns and trends related to infrastructure vulnerabilities and their interdependencies. 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: B.S or an M.S in computational data sciences and engineering, applied mathematics, geography, statistics, computer engineering or equivalent field with 0-2 years of research experience. Strong background in quantitative analysis, mathematical modeling, data science and visualization techniques. Proficiency in programming languages such as Python, Java, Git, SQL etc. and experience with relevant tools and frameworks for both structured and unstructured data analysis and machine learning. Experience with database technologies, particularly to store, analyze, and manipulate spatio-temporal data. Excellent written and oral communication skills including peer-reviewed publications, with the ability to collaborate effectively with colleagues from diverse backgrounds and present technical information to various audiences. Proven ability to work independently and as part of a team, with a strong commitment to achieving project goals and delivering high-quality results. Keen learner with a hunger to learn about new emerging problem areas and their socioeconomic impact on populations, terrains and other infrastructural assets Preferred Qualifications: Knowledge of infrastructure systems, including energy, transportation, water, and telecommunications, and their interactions with social and economic factors. Publication record demonstrating contributions to peer-reviewed journals and conferences in relevant domains. Familiarity with industry standards and best practices for grid resilience assessments, such as NERC CIP standards, as well as equity frameworks and methodologies. GIS analysis for siting studies, National Environmental Policy Act, transload site identification, mode and route analysis Experience working collaboratively in version control systems for source code management such as Git/GitLab. Knowledge of major challenges in modeling and representing the built environment. Ability to function well in a fast-paced research environment, set priorities to accomplish multiple tasks within deadlines, and adapt to ever changing needs.