Post-Doctoral Associate - Structural Digital Twin

New York University; Al Ruways Industrial City; On-site; AED 22,000 - 32,000; Full time

Benefits offered by this job

Medical insurance

Housing allowance

Annual home-leave travel

Educational subsidies

Job summary

NewYork University AbuDhabi invites applications for a Post-Doctoral Associate in Structural Digital Twin. The role involves developing ML-enabled digital twins for civil structures, integrating data from tests, sensors, and simulations, and advancing computational models within a multidisciplinary SHORES-centered program.

The successful candidate will work with faculty, postdocs, and engineers to validate models, publish results, and contribute to project proposals while ensuring research

Qualifications

• PhD in Civil Engineering, Engineering Mechanics, Mechanical Engineering, or closely related field.
• No more than five years of research or professional experience after the PhD.
• Demonstrated research experience in structural modeling and machine learning model development.
• Excellent technical writing, presentation, and interdisciplinary communication skills.

Responsibilities

• Develop computational models that represent the structural response, deterioration, and performance of civil engineering systems.
• Create machine learning and artificial intelligence models for predicting structural behavior under different loading and environmental conditions.
• Integrate numerical models, sensor measurements, laboratory observations, and experimental findings within a digital twin framework.
• Conduct structural and geotechnical simulations using commercial and research‑oriented engineering software.
• Design physical experiments that generate reliable data for model calibration and validation.
• Support laboratory testing involving structural components, soil‑structure systems, or geotechnical applications.
• Configure instrumentation and data‑acquisition systems for structural sensing and health monitoring.
• Process time‑series, sensor, experimental, and simulation data using appropriate computational methods.
• Compare digital twin predictions with physical test results and monitoring measurements.
• Quantify model accuracy, uncertainty, sensitivity, and generalization across different structural conditions.
• Refine computational and machine learning models based on observed discrepancies.
• Develop reproducible data‑processing, model‑training, and validation workflows.
• Maintain organized research code, datasets, technical documentation, and experimental records.
• Collaborate with faculty members, postdoctoral researchers, engineers, students, and multidisciplinary project partners.
• Prepare manuscripts for peer‑reviewed journals and present findings at conferences, seminars, and research meetings.
• Contribute to research proposals, progress reports, technical presentations, and future project development.
• Follow laboratory safety, research ethics, data‑management, and institutional compliance requirements.

Skills

Structural engineering

Structural digital twins

Machine learning

Artificial intelligence

Structural modeling

Geotechnical modeling

Engineering mechanics

Finite element analysis

Computational mechanics

Structural health monitoring

Education

PhD in Civil Engineering

Job description

Job Title

Post-Doctoral Associate – Structural Digital Twin

Job Details

Location: Abu Dhabi, United Arab Emirates
Industry: Higher Education
Function: Research
Job type: Full-time
Salary: 22,000‑32,000 (Estimated)

Overview

Post-Doctoral Associate – Structural Digital Twin is a research opportunity at NewYork University Abu Dhabi. The role focuses on machine‑learning‑powered digital twins, structural engineering, geotechnical modeling, sensing, and infrastructure health monitoring.

Role Context

The associate will join the Center for Sand Hazards and Opportunities in Resilience, Energy, and Sustainability and the Division of Engineering. Working primarily with research groups led by Professors TarekAbdoun and MostafaMobasher, the candidate will help create a digital twin platform capable of representing, monitoring, and forecasting the behavior of civil engineering structures. The research combines structural and geotechnical simulation, artificial intelligence, physical testing, sensing systems, and model validation.

Core Duties

• Develop computational models that represent the structural response, deterioration, and performance of civil engineering systems.
• Create machine learning and artificial intelligence models for predicting structural behavior under different loading and environmental conditions.
• Integrate numerical models, sensor measurements, laboratory observations, and experimental findings within a digital twin framework.
• Conduct structural and geotechnical simulations using commercial and research‐oriented engineering software.
• Design physical experiments that generate reliable data for model calibration and validation.
• Support laboratory testing involving structural components, soil‑structure systems, or geotechnical applications.
• Configure instrumentation and data‑acquisition systems for structural sensing and health monitoring.
• Process time‑series, sensor, experimental, and simulation data using appropriate computational methods.
• Compare digital twin predictions with physical test results and monitoring measurements.
• Quantify model accuracy, uncertainty, sensitivity, and generalization across different structural conditions.
• Refine computational and machine learning models based on observed discrepancies.
• Develop reproducible data‑processing, model‑training, and validation workflows.
• Maintain organized research code, datasets, technical documentation, and experimental records.
• Collaborate with faculty members, postdoctoral researchers, engineers, students, and multidisciplinary project partners.
• Prepare manuscripts for peer‑reviewed journals and present findings at conferences, seminars, and research meetings.
• Contribute to research proposals, progress reports, technical presentations, and future project development.
• Follow laboratory safety, research ethics, data‑management, and institutional compliance requirements.

Ideal Profile

• PhD in Civil Engineering, Engineering Mechanics, Mechanical Engineering, or a closely related field.
• No more than five years of research or professional experience after receiving the PhD.
• Demonstrated research experience in structural modeling and machine learning model development.
• Strong understanding of structural mechanics, numerical simulation, and civil infrastructure behavior.
• Experience developing computational representations of structural performance using commercial or research software.
• Practical ability to develop, train, test, and validate artificial intelligence or machine learning models.
• Knowledge of structural sensing, health monitoring, instrumentation, or data‑acquisition systems is preferred.
• Experience conducting physical experiments for structural or geotechnical applications is advantageous.
• Ability to validate modeling results against laboratory, field, or monitoring data.
• Strong programming and quantitative data‑analysis skills.
• Familiarity with finite element analysis, digital twin architecture, signal processing, or uncertainty quantification is beneficial.
• Research publication record appropriate for a postdoctoral appointment.
• Excellent technical writing, presentation, and interdisciplinary communication skills.
• Ability to lead independent research activities while contributing effectively to a collaborative team.
• Emirati candidates are encouraged to apply.

Skills Set

• Structural engineering
• Structural digital twins
• Machine learning
• Artificial intelligence
• Structural modeling
• Geotechnical modeling
• Engineering mechanics
• Finite element analysis
• Computational mechanics
• Structural health monitoring
• Structural sensing
• Data acquisition
• Sensor data processing
• Physical experimentation
• Geotechnical testing
• Model calibration
• Model validation
• Predictive modeling
• Time‑series analysis
• Signal processing
• Scientific programming
• Experimental data analysis
• Simulation data analysis
• Uncertainty quantification
• Infrastructure resilience
• Soil‑structure interaction
• Research documentation
• Scientific publication
• Multidisciplinary collaboration
• Research proposal development

Why Join Us

This position provides an opportunity to work on an advanced digital twin platform with direct relevance to resilient infrastructure, structural safety, and data‑driven engineering. The successful candidate will combine physical experiments, engineering simulation, sensing technology, and machine learning within a multidisciplinary research program.

Employment terms include a competitive salary, medical insurance, housing allowance, annual home‑leave travel, educational subsidies for eligible children, and other research staff benefits. The appointment also offers access to advanced computing and laboratory facilities, international collaboration, professional development, and opportunities to publish influential work in structural engineering and artificial intelligence.

About the Company

NewYork University AbuDhabi is an internationally connected research university supporting advanced scholarship across engineering, science, social sciences, humanities, and the arts. Through the SHORES Center and Division of Engineering, the university develops innovative solutions for infrastructure resilience, structural performance, geotechnical systems, energy, and sustainability.