Postdoctoral Research Associate

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

Job summary

Northeastern University is seeking a Postdoctoral Associate to conduct integrated experimental and computational research on informatics-driven mechanical characterization of solid and porous materials. The role combines hands-on experimental mechanics with rigorous simulation and data analysis, including DIC, FE workflows, and Bayesian optimization.

The successful candidate will design heterogeneous specimens, perform multiaxial tests on a dual-actuator frame, and develop automated simulation

Qualifications

• PhD in mechanical engineering, aerospace engineering, materials science and engineering, civil engineering, or a closely related field.
• 0-3 years of relevant research experience in experimental and/or computational solid mechanics
• Knowledge of solid mechanics and constitutive modeling including anisotropic elasticity, plasticity, and porous/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 fabrication, instrumentation, and test documentation
• Knowledge of nonlinear finite element analysis and experience with codes such as Abaqus (including user-defined materials)
• 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 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 experimental platform by designing heterogeneous specimens and conducting multiaxial tests with a dual-actuator frame
• Build automated FE and inverse-identification workflows that learn constitutive parameters from test data and perform cross-validation with uncertainty quantification
• Extend modeling framework to porous and cellular materials, including compressible formulations and transferability assessment
• Support industry-sponsored projects on testing and characterization of battery components and related materials
• Document findings through publications, reports, and conference presentations; mentor students; contribute to proposals

Skills

Python programming

DIC (digital image correlation)

Bayesian inference

Finite element methods

data analysis

experimental mechanics

uncertainty quantification

Education

PhD in mechanical engineering or closely related field

Tools

Abaqus

Open-source FEM tools

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

• 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

• 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

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. All qualified applicants are encouraged to apply and will receive consideration for employment without regard to race, religion, color, national origin, age, sex, sexual orientation, disability status, or any other characteristic protected by applicable law.

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.

Position Type

Research

Company Overview

Founded in 1898, Northeastern is a global research university and the recognized leader in experiential lifelong learning. Our approach of integrating real‑world experience with education, research, and innovation empowers our students, faculty, alumni, and partners to create worldwide impact. Our global university system provides our community and academic, government, and industry partners with unique opportunities to think locally and act globally. The system—including 14 campuses across the U.S., U.K., and Canada, 300,000‑plus alumni, and 3,000 partners worldwide—serves as a platform for scaling ideas, talent, and solutions. The university’s residential campuses for undergraduate and graduate degrees are located in Boston, London, and Oakland, California. Our research and graduate campuses are in the Massachusetts communities of Burlington and Nahant; Arlington, Virginia; Charlotte, North Carolina; Miami; Portland, Maine; Seattle; Silicon Valley, California; Toronto; and Vancouver. Northeastern’s personalized, experiential undergraduate and graduate programs lead to degrees through the doctorate across 10 colleges and schools across our campuses. Learning emphasizes the intersection of data, technology, and human literacies, uniquely preparing graduates for careers of the future and lives of fulfillment and accomplishment. Our research enterprise, with an R1 Carnegie classification, is solutions oriented and spans the world. Our faculty scholars and students work in teams that cross not just disciplines, but also sectors—aligned around solving today’s highly interconnected global challenges and focused on transformative impact for humankind.