H
Not Specified Permanent

Malibu, California · USA job

Computational Scientist, Metal Additive Manufacturing

HRL Laboratories

Malibu, California

Job description

Overview

In this role, you will advance metal additive manufacturing through analytical and computational modeling, collaborating with interdisciplinary teams to drive breakthroughs. You will translate materials science insights into scalable, real-world solutions and help steer research toward impactful outcomes. The position sits on a high-profile America Makes-funded project and offers a path toward leading new initiatives as a future Principal Investigator. You will present results to sponsors and the broader community, shaping the strategic direction of the program.

Compensation / Benefits
  • medical, dental, vision
  • life insurance
  • 401K match
  • gym facilities
  • PTO
  • sick time
Responsibilities
  • Support advanced R&D programs in metal additive manufacturing with interdisciplinary teams
  • Plan and execute analytical and computational analyses focusing on continuum methods and digital twin development
  • Communicate complex modeling results via presentations, reports, and discussions with stakeholders
  • Translate fundamental materials understanding into practical additive manufacturing solutions
  • Plan and manage project components including timelines, deliverables, and resources
  • Collaborate with experimental teams to validate models and refine predictive capabilities
  • Present at conferences and publish in journals
Key requirements
  • PhD or Postdoctoral experience in Materials Science, Mechanical Engineering, or a related field
  • Strong foundation in computational modeling and familiarity with AI/ML for materials/process optimization
  • Experience with Finite Element Analysis (FEA) such as ABAQUS, COMSOL, MOOSE
  • Experience with mesoscale microstructure modeling techniques (phase field, cellular automata, kinetic Monte Carlo)
  • Proficiency in Python or C++ for data analysis and simulation workflows
  • Experience with ML libraries (Scikit-Learn, PyTorch, Keras, TensorFlow) for engineering problems
  • Experience with Scientific Machine Learning (SciML) for surrogate models
  • Experience with scientific communication through presentations and publications
  • clear communicator
  • cross-functional collaboration
  • ability to present technical results to sponsors
  • FEA (ABAQUS, COMSOL, MOOSE)
  • phase-field, cellular automata, kinetic Monte Carlo
  • Python, C++

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