Computational Scientist

Description

The Leidos Research Support Team supporting the National Energy Technology Laboratory (NETL) is seeking a Computational Scientist to join our team in Pittsburgh, PA. This opportunity will allow side by side execution of research with world-class scientists and engineers using state of the art equipment to contribute to new areas of basic and applied research. The work will support the development of advanced physics-based models for understanding mechanical, structural, and fluid dynamics in natural gas infrastructure. A core focus of this role is to integrate Computational Fluid Dynamics (CFD) and fluid-structure interaction (FSI) into gas pipeline modeling, as well as to accelerate the training of AI and ML models. The successful candidate will collaborate with an interdisciplinary team to create Multiphysics-based models that will be validated through experimental and field tests, including real-world data collection from natural gas pipelines.

The researcher will also apply advanced data analytics methods to classify spatial, temporal, and frequency-dependent features of optical fiber-based distributed sensing data. These models and data will be used to predict incipient failures, leveraging distributed optical fiber sensing, wireless sensor technologies, and other infrastructure monitoring tools, including subsurface systems. In this position, the researcher will have opportunities to publish in high-quality peer-reviewed journals, present at national and international conferences, and contribute to the development of new intellectual property. Regular project reporting will be required. Given the extensive interface with clients and collaborators, strong communication skills are essential.

Location: This position is based at the NETL lab in Pittsburgh, PA. Only candidates near (or willing to relocate to) the Pittsburgh, PA area will be considered.

Primary Responsibilities:

• Develop advanced physics-based models for gas pipeline failures, focusing on mechanical and structural integrity.
• Utilize Computational Fluid Dynamics (CFD) for gas flow modelling and incorporate fluid-structure interaction (FSI) for comprehensive pipeline analysis.
• Build digital twin models of natural gas pipelines for real-time monitoring and failure prediction.
• Employ reduced order modeling, surrogate modeling, and physics-informed machine learning to enhance predictive capabilities.
• Work with an interdisciplinary team to develop Multiphysics-based models and conduct field validation for data collection.
• Apply advanced data analytics methods (PCA, neural networks, machine learning, big data analytics) to distributed sensing data.
• Utilize optical fiber-based distributed sensing for predictive monitoring of gas pipeline infrastructure.
• Engage in data analytics for wireless sensor technology platforms and subsurface monitoring.
• Publish research in peer-reviewed journals and present at technical conferences.
• Develop new intellectual property based on innovative research.
• Provide regular project updates and reports to project managers and clients.

Required Education & Experience:

• Masters degree in Mechanical Engineering, Applied Physics, Materials Engineering, Mathematics, Data Science, Computer Science, Electrical Engineering, or a related field, with 2+ years of relevant experience (PhD preferred).
• Strong knowledge of multi-physics and finite element modeling for mechanical, structural, and gas flow analysis in pipelines.
• Experience with Computational Fluid Dynamics (CFD) for gas flow modeling and fluid-structure interaction (FSI).
• Experience with ANSYS multi-physics software platform or any other open-source modeling tools for CFD, FSI related modelling.
• Expertise in advanced data analytics methods, including AI, neural networks, machine learning, and big data analytics.
• Proficiency with reduced order modeling, surrogate modeling, digital twin development, and physics-informed machine learning.
• Experience with software such as ANSYS twinbuilder, NVIDIA modulus or any other related open source software for digital twin building.
• Familiarity with high-performance computing environments and Multiphysics modeling tools.
• Excellent communication skills and the ability to work in an interdisciplinary team environment.

Preferred Qualifications:

• Post-doctoral experience in a government-owned national laboratory
• Expertise in machine learning, artificial intelligence, and big data analytics for sensing data processing
• Experience with optical fiber-based distributed sensing data analysis and early detection of failures and leaks within natural gas infrastructure
• Familiarity with digital twin models, reduced order modeling, and physics-informed machine learning, CFD and FSI modeling for gas pipelines

Salary Range for this position: $100K to $110K

While subject to change based on business needs, Leidos reasonably anticipates that this job requisition will remain open for at least 3 days with an anticipated close date of no earlier than 3 days after the original posting date as listed above.

The Leidos pay range for this job level is a general guideline onlyand not a guarantee of compensation or salary. Additional factors considered in extending an offer include (but are not limited to) responsibilities of the job, education, experience, knowledge, skills, and abilities, as well as internal equity, alignment with market data, applicable bargaining agreement (if any), or other law.