Computational Materials Modeling Postdoctoral Researcher

Search Jobs - INL Professional Careers; Idaho Falls (ID); Sur place; USD 95 000 - 116 000; Plein temps

Résumé du poste

Idaho National Laboratory invites applications for a Postdoctoral Research Associate in Physics or Engineering disciplines to develop a hybrid experimental/computational framework at our Idaho Falls site. The role spans from microstructure measurements to full-system performance predictions and requires work with data, experiments, and physics-based machine-learning models.

You will write reports and manuscripts for publication and may present results at national conferences.

Qualifications

• :

Responsabilités

• Develop a hybrid experimental/computational framework to evaluate material performance in engineered systems.
• Span microstructure measurements to performance predictions in full-scale systems.
• Implement high-fidelity models and create surrogates, with uncertainty quantification.
• Produce reports and manuscripts; present results at national conferences.
• Collaborate with data/experiment/physics‑based ML teams.

Connaissances

C++

Python

Matlab

R

Octave

Willingness to learn

Formation

PhD in Physics

PhD in Mechanical/Aerospace Engineering

PhD in Materials Science

PhD in Applied Mathematics

Outils

LS‑DYNA

Abaqus Explicit

Ansys Explicit

Peridigm

Cubit

GMSH

Hypermesh

SLURM

PBS

Linux HPC

Description du poste

Idaho National Laboratory is hiring a well-qualified Postdoctoral Research Associate in Physics, Mechanical Engineering, Aerospace Engineering, Materials Science, Applied Mathematics, or a comparable discipline for National Security Solutions (D110). Our team works a 9x80 schedule located out of our Idaho Falls facility with every other Friday off.

Successful candidates will be helping to develop a hybrid experimental/computational framework to support evaluation of material performance in engineered systems. This framework will span from microstructure measurements to performance predictions in full-scale systems. The team will implement traditional, high-fidelity models, create simplified surrogates, and quantify the uncertainties in these analyses. This effort will require working with other teams that provide data, run experiments, and develop physics-based machine-learning models.

The candidate is expected to be a strong asset to our interdisciplinary R&D team and work effectively both alone and as a team member. The candidate must be self-motivated but also take direction from other team members and peers.

The position entails writing reports, scientific manuscripts for publication, and potentially presentation of research results at national conferences and meetings. Much of the publication and presentation will be limited distribution.

Required:

• PhD in in Physics, Mechanical Engineering, Aerospace Engineering, Materials Science, Applied Mathematics or comparable disciplines.
• PhD requirements must be completed by commencement of appointment and within the previous 5 years.
• Ability to get a DOE L clearance, which requires US citizenship.
• Demonstrated oral and published written communication skills in English.
• Familiarity with data analysis languages and tools such as C++, Python, Matlab, R, Octave, or similar.
• Willingness to learn by doing.
  

The ideal candidate will possess:

• Candidates must demonstrate experience with the development of surrogate models, the quantification of uncertainties, and expertise in one or more of the following areas:
  • Microstructure reconstruction from EBSD and similar datasets and surrogate microstructure generation
    • Crystal plasticity modeling with surrogate and reconstructed meshes
  • Physics-based fatigue modeling.
    • Crack growth modeling
    • Microstructural dependent fatigue life prediction
  • Dynamic material response
    • Design and analysis of experiments using laser shock, parallel plate impact, Kolsky Bar, or similar.
    • Continuum scale explicit dynamic modeling. Ideally this should include use of software tools such as Abstrao, LS dyna, Abaqus Explicit, Ansys Explicit, Peridigm, CTH, ALEGRA, ALE3D, EPIC, Sierra, or similar tools.
      Mesh generation tools such as Cubit, GMSH, Hypermesh or similar.
      Linux OS especially in a high performance computing environment.
      SLURM, PBS, or other job scheduling tools commonly used on HPC systems
      

Physical Requirements:

While performing the duties of this classification, the employee is frequently required to stand, walk, sit, stoop, kneel, bend. The job requires hand/finger dexterity to keyboard or type, handle materials, manipulate tools, and reach with hands and arms. The job requires operation of job-related equipment. The employee must occasionally lift and/or move up to 25 pounds. Sufficient visual acuity and hearing capacity to perform the essential functions and interact with people is required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.

Job Information:

• The pay for this position is $105,144.00 Annually. At Idaho National Laboratory compensation decisions are determined using factors such as education, relevant experience, and other credentials.
• TDP: This is a testing designated position; you will be required to submit to a pre-employment drug screen and periodic drug testing throughout the term of your employment.