AI-Driven Detonation Modeling Postdoc

Los Alamos National Laboratory; Ann Arbor (MI); Vor Ort; USD 70.000 - 90.000; Vollzeit

Benefits dieser Stelle

Medical and dental insurance

401(k) with matching

Relocation assistance

Zusammenfassung

Los Alamos National Laboratory (LANL) invites applications for a postdoctoral position within the SPARC program, located on the University of Michigan campus in Ann Arbor, MI. The role focuses on developing computational tools to calibrate detonation models for energetic materials and integrating AI/ML orchestration to quantify model reliability.

Initial milestones include AI-driven adaptive calibration, new constitutive closures for high-speed reacting flows, and multi-fidelity reduced-order

Qualifikationen

• A Ph.D. in Computational Science, Engineering, or Applied Mathematics completed within the last five years or soon to be completed.
• Background in high-speed compressible flow physics of gaseous and/or condensed phase materials.
• Proficiency in Python, modern Fortran, and/or C++.
• Strong record of publications commensurate with experience.
• Strong oral and written communication skills.
• Ability to obtain a DOE Q Clearance, which typically requires US Citizenship

Aufgaben

• Develop AI-driven frameworks for adaptive model calibration and experimental design
• Derive novel modeling paradigms and constitutive closures for high-speed reacting flows
• Develop reduced-order modeling capabilities across a multi-fidelity simulation hierarchy
• Inform experimental design at LANL for calibration and validation of energetic materials

Kenntnisse

Python

Fortran

C++

Publications

Communication skills

Ausbildung

Ph.D. in Computational Science, Engineering, or Applied Mathematics

Jobbeschreibung

Los Alamos National Laboratory (LANL) invites applications for a postdoctoral position within the SPARC program, located on the University of Michigan campus in Ann Arbor, MI. The role focuses on developing computational tools to calibrate detonation models for energetic materials and integrating AI/ML orchestration to quantify model reliability.

Initial milestones include AI-driven adaptive calibration, new constitutive closures for high-speed reacting flows, and multi-fidelity reduced-order