Computational Scientist, -scale Simulations and Machine Learning

Requisition Id15757

We are seeking a Computational Scientist for the Multiscale Materials group in the Computational Science and Engineering Division (CSED). The candidate will be expected to contribute to the development and application of advanced manufacturing simulations, and machine learning (ML) models relevant to additive manufacturing, virtual manufacturing, material characterization, topology optimization, and real-time sensing. CSED focuses on transdisciplinary computational science and analytics at scale to enable scientific discovery across the physical sciences, engineered systems, and biomedicine and health.

It provides foundations and advances in quantum information sciences to enable quantum computers, devices, and networked systems. It develops community applications, data assets, and technologies and provides assurance to build knowledge and impact in novel, crosscut-science outcomes.

• Collaborate within a multi-disciplinary research environment consisting of computational scientists, computer scientists, experimentalists, and engineers/physicists conducting basic and applied research in support of the Laboratory's missions.
• Participate in the development of multi-physics simulations with machine learning algorithms for modeling and optimizing of metallic materials and advanced manufacturing processes.
• Participate in the design of integrated, scalable numerical methods and uncertainty quantification, and follow team planning, documentation, verification and validation, and manufacturing-material-related simulation quality processes.
• Author peer reviewed papers, technical papers, reports and proposals for internal and external release as well as represent the organization by giving technical presentations in large public forums.

• A PhD degree in mechanical engineering, material science, or a relevant engineering field with emphasis on computational and material science. 2 years of applicable experience outside of Ph.D.
• Expertise in the development and application of multi-physics simulations and machine learning tools in one or more areas such as melt pool fluid dynamics, distortion and residual stress prediction, microstructure quantification, topology optimization, and surrogate and reduced-order models.
• Experience working in a multi-disciplinary research environment.
• Proven publication track record.
• Effective interpersonal skills, written and oral communication skills, and strong motivation.

• Demonstrated experience in the design and implementation of numerical simulations and machine learning algorithms in one or more areas within advanced manufacturing processes.
• Demonstrated experience in the design and implementation of numerical algorithms in one or more high-level computing languages, preferably within a team that follows software quality standards.
• Excellent written and oral communication skills.
• Motivated self-starter with the ability to work independently and to participate creatively in collaborative teams across the laboratory.
• Ability to function well in a fast‑paced research environment, set priorities to accomplish multiple tasks within deadlines, and adapt to ever changing needs.

Letters of Recommendation: 3 number of references are required.

Please submit three letters of reference when applying to this position. You may upload these directly to your application or have them sent to ORNLRecruiting the position title and number referenced in the subject line.

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