Graduate Research Associate - Computational Mechanics

Los Alamos National Laboratory

Los Alamos (NM)

Vor Ort

USD 50.971 - 57.859

Vollzeit

14 Tage+

Eine vollständige Bewerbung in einer Minute

Maßgeschneiderter Lebenslauf und Anschreiben – versandbereit.

Schaffe es an den ATS-Filtern vorbei

Benefits dieser Stelle

Medical insurance
Dental and vision insurance
Relocation assistance
Tuition assistance
Flexible schedules
Onsite gym

Zusammenfassung

Los Alamos National Laboratory is seeking a graduate research assistant in the Fluid Dynamics and Solid Mechanics Group to explore computational solid mechanics, fracture mechanics, and related numerical methods.

The work focuses on modeling material dynamics, coupling advanced models with FEM/MPM approaches across scales, and collaboration with DOE/NNSA partners. Ideal candidates are pursuing a Master’s or PhD in relevant fields.

Qualifikationen

  • Enrollment in a Master’s or Doctoral degree in computational mechanics, mechanical engineering, civil engineering, or applied mathematics.
  • GPA of at least 3.2 on graduate coursework.
  • Ability to work within a collaborative team environment.
  • Desire to learn new things and openness to work on different projects.

Aufgaben

  • Conduct research spanning computational solid mechanics, fracture mechanics, geomechanics and development of numerical methods.
  • Model material dynamics across multiple length scales and couple advanced models with numerical methods.
  • Collaborate with multidisciplinary teams and DOE/NNSA partners.

Kenntnisse

Collaborative work
Willingness to learn
FEM/MPM programming
Research in fluid/solid dynamics

Ausbildung

Master's or PhD in Computational Mechanics
GPA ≥ 3.2 on graduate coursework

Jobbeschreibung

Los Alamos National Laboratory is seeking a graduate research assistant in the Fluid Dynamics and Solid Mechanics Group to explore computational solid mechanics, fracture mechanics, and related numerical methods.

The work focuses on modeling material dynamics, coupling advanced models with FEM/MPM approaches across scales, and collaboration with DOE/NNSA partners. Ideal candidates are pursuing a Master’s or PhD in relevant fields.

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