Postdoctoral Research Associate

Northeastern University; New Jersey; On-site; USD 60,000 - 85,000; Full time

Job summary

Northeastern University, located in New Jersey, is seeking a Postdoctoral Associate to conduct integrated experimental and computational research in informatics-driven mechanical characterization of materials. The role combines hands-on experiments with rigorous simulation and data analysis, including DIC, FE workflows, and Bayesian inference.

The ideal candidate will have a PhD in a relevant field, strong programming skills in Python, experience with Abaqus, and a background in uncertainty

Qualifications

• PhD in mechanical engineering or closely related field.
• Zero to three years of relevant research experience in experimental and/or computational solid mechanics.
• Knowledge of solid mechanics and continuum constitutive modeling including anisotropic elasticity, plasticity, and porous or cellular materials.
• Ability to conduct mechanical testing on electromechanical or servo-hydraulic load frames and apply full-field optical measurement techniques such as DIC.
• Ability to design and execute experiments on heterogeneous specimen geometries, including specimen fabrication, instrumentation, and test documentation.
• Knowledge of nonlinear finite element analysis and experience with commercial or open-source finite element codes, such as Abaqus, including user-defined material subroutines.
• Ability to program in Python and develop automated simulation, data-processing, and inverse-identification workflows.
• Familiarity with optimization, Bayesian inference, uncertainty quantification, or machine learning methods applied to engineering problems.
• Ability to analyze and interpret experimental and simulation data, prepare peer-reviewed publications, and present results to academic and industrial audiences.

Responsibilities

• Develop and operate the experimental platform for informatics-driven characterization by designing and fabricating heterogeneous specimens, conducting multiaxial mechanical tests on the dual-actuator loading frame, and acquiring and processing full-field deformation data using three-dimensional digital image correlation.
• Develop the numerical platform by building automated finite element and inverse-identification workflows that learn constitutive parameters from global and full-field test data and performing systematic cross-validation between measured and simulated responses, including uncertainty quantification.
• Extend the modeling framework to porous and cellular materials, such as polymer and metal foams, including compressible constitutive formulations, calibration against experimental data, and assessment of the framework’s transferability across material classes.
• Support industry-sponsored projects on the mechanical testing and characterization of battery components and related material systems, including test planning, execution, data analysis, and preparation of technical deliverables for sponsors.
• Document and disseminate research findings through peer-reviewed publications, project reports, and conference presentations; mentor graduate and undergraduate students; and contribute to proposal preparation and collaborative research efforts

Skills

Python programming

Finite element analysis

Bayesian inference

Uncertainty quantification

DIC (3D Digital Image Correlation)

Machine learning (engineering)

Experimental mechanics

Data analysis

Education

PhD in mechanical engineering or closely related field

Tools

Abaqus

Python

Finite element software

Job description

About the Opportunity

Job Summary

The Postdoctoral Associate will conduct integrated experimental and computational research on informatics-driven mechanical characterization of solid and porous materials, supported by an NSF CMMI CDS&E award and industry-sponsored projects. The primary function of the position is to develop and cross-validate the experimental and numerical platforms of the mechanics informatics framework. On the experimental side, this involves designing and fabricating heterogeneous specimens, conducting multiaxial mechanical tests on a dual-actuator loading frame, and acquiring full-field deformation data using three-dimensional digital image correlation (DIC). On the numerical side, this involves building automated finite element and inverse-identification workflows that learn constitutive parameters from a single informative test through Bayesian optimization, together with quantification of measurement and model-form uncertainty. Approximately half of the effort will extend this framework to the constitutive modeling of porous and cellular materials, such as polymer and metal foams, and to industry-sponsored projects on the mechanical testing and qualification of battery components. The role combines hands-on experimental mechanics with rigorous simulation and data analysis and benefits from complementary expertise in scientific machine learning and constitutive modeling of anisotropic and compressible materials.

Minimum Qualifications

Required

• PhD in mechanical engineering, aerospace engineering, materials science and engineering, civil engineering, or a closely related field
• Zero to three years of relevant research experience in experimental and/or computational solid mechanics
• Knowledge of solid mechanics and continuum constitutive modeling, including anisotropic elasticity, plasticity, and the mechanics of porous or cellular materials
• Ability to conduct mechanical testing on electromechanical or servo-hydraulic load frames and apply full-field optical measurement techniques such as digital image correlation
• Ability to design and execute experiments on heterogeneous specimen geometries, including specimen fabrication, instrumentation, and test documentation
• Knowledge of nonlinear finite element analysis and experience with commercial or open-source finite element codes, such as Abaqus, including user-defined material subroutines
• Ability to program in Python and develop automated simulation, data-processing, and inverse-identification workflows
• Familiarity with optimization, Bayesian inference, uncertainty quantification, or machine learning methods applied to engineering problems
• Ability to analyze and interpret experimental and simulation data, prepare peer-reviewed publications, and present results to academic and industrial audiences

Preferred

• Experience with the mechanical characterization of battery components, foams, or other porous and layered material systems

Key Responsibilities & Accountabilities

• Develop and operate the experimental platform for informatics-driven characterization by designing and fabricating heterogeneous specimens, conducting multiaxial mechanical tests on the dual-actuator loading frame, and acquiring and processing full-field deformation data using three-dimensional digital image correlation
• Develop the numerical platform by building automated finite element and inverse-identification workflows that learn constitutive parameters from global and full-field test data and performing systematic cross-validation between measured and simulated responses, including uncertainty quantification
• Extend the modeling framework to porous and cellular materials, such as polymer and metal foams, including compressible constitutive formulations, calibration against experimental data, and assessment of the framework’s transferability across material classes
• Support industry-sponsored projects on the mechanical testing and characterization of battery components and related material systems, including test planning, execution, data analysis, and preparation of technical deliverables for sponsors
• Document and disseminate research findings through peer-reviewed publications, project reports, and conference presentations; mentor graduate and undergraduate students; and contribute to proposal preparation and collaborative research efforts

Required Documents

• Resume

Position Type

Research

Additional Information

Northeastern University considers factors such as candidate work experience, education and skills when extending an offer.

Northeastern has a comprehensive benefits package for benefit eligible employees. This includes medical, vision, dental, paid time off, tuition assistance, wellness & life, retirement- as well as commuting & transportation. Visit https://hr.northeastern.edu/benefits/ for more information.

Compensation Grade/Pay Type:
108S

Expected Hiring Range:
$60,315.00 - $85,192.50

With the pay range(s) shown above, the starting salary will depend on several factors, which may include your education, experience, location, knowledge and expertise, and skills as well as a pay comparison to similarly-situated employees already in the role. Salary ranges are reviewed regularly and are subject to change.