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David Morin

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PhD Candidate in AI-driven Smart Material Characterisation and Modelling Norwegian University of Science and Technology in Norway

Degree Level

PhD

Field of study

Materials Science

Funding

PhD Candidate position with gross salary normally NOK 580,000 per annum, 3-year employment period.

Deadline

Nov 20, 2026

Country flag

Country

Norway

University

Norwegian University of Science and Technology

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Keywords

Materials Science
Artificial Intelligence
Structural Engineering
Material Characterization
Offshore Engineering
Fluid-structure Interaction
Materials Physics
Computational Modelling
Smart Material
Finite Element Analysis
ML

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About this position

PhD opportunity at the Department of Structural Engineering, NTNU in Trondheim, focused on AI-driven smart material characterisation and modelling for future offshore structures.

The project is part of AIMOS (Artificial Intelligence for Multiscale Offshore Structures), an initiative developing integrated AI methods for the analysis, design, monitoring, and control of floating offshore systems. The PhD researcher will study smart materials that could be used in adaptive offshore structures, with emphasis on how these materials behave mechanically under changing environmental and operational conditions.

The research combines experimental investigations, physics-informed modelling, and artificial intelligence to build predictive material models. The work aims to understand and represent material behaviour such as stiffness variation, response times, and long-term durability, while creating computationally efficient models suitable for structural simulation. The candidate will also evaluate the feasibility of smart-material-based actuation in offshore engineering applications.

The position is embedded in a broader interdisciplinary environment at NTNU, with collaboration expected across the AIMOS project and with other PhD candidates working on fluid-structure interaction and agentic AI. The candidate will also participate in the SIMLab research group at the department, contributing to research at the intersection of material mechanics, machine learning, and marine structural engineering.

The position is at NTNU, the Norwegian University of Science and Technology, within the Department of Structural Engineering, which conducts internationally leading research and participates in large national research projects.

Funding details

PhD Candidate position with gross salary normally NOK 580,000 per annum, 3-year employment period.

What's required

Applicants must hold a relevant Master's degree in mechanical engineering, computer science, or equivalent, with a course of study corresponding to a five-year Norwegian degree and 120 credits at master's level. A completed Master's degree must be documented before starting the position and no later than 2027-01-01. Applicants must have a strong academic background with an average grade of B or better on NTNU's grading scale or an equally strong academic foundation, and weaker grades may be offset by particularly strong suitability for PhD education. Very good English skills are required, and applicants from non-English speaking countries outside Europe must provide an official language test report; accepted tests are TOEFL, IELTS, CAE, or CPE with the stated minimum scores. Preferred qualifications include a strong background in mechanical or structural engineering, material mechanics, finite-element analysis, computational plasticity, Python programming, AI and machine learning for engineering, and experimental work or data analysis.

How to apply

Apply electronically via Jobbnorge.no before the deadline. Include transcripts and diplomas for Bachelor's and Master's degrees, a CV, copy of Master's thesis or draft, a project outline, a motivation letter, any relevant publications, and contact information for three referees. If applicable, also attach documentation of foreign education, diploma supplements, and any HK-dir statement.

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Professors

Associate Professor

University Name
Country flag

Norway

Research Interests

Offshore Engineering

Structural Engineering

Materials Science

Material Characterization

Ml