PhD in AI-Driven Smart Materials for Offshore Structures

NTNU - Norwegian University of Science and Technology; Trondheim; On-site; NOK 522,000 - 638,000; Full time

Benefits offered by this job

Mentor programme

Pension benefits (SPK)

Career guidance

Job summary

NTNU in Trondheim invites applications for a PhD Candidate position focused on AI-driven smart material characterisation and modelling within offshore structures. The role combines experimental work, physics-informed modelling, and machine learning to develop predictive material models for adaptive marine infrastructure.

The successful candidate will work in a leading, international research environment, contribute to academic publications, and participate in cross-disciplinary AIMOS activities

Qualifications

• Master's degree in mechanical engineering, computer science, or equivalent; 120 credits at master's level.
• Strong English skills (written and spoken).
• Admission to NTNU's doctoral program.
• Applicants from non-English-speaking countries must present language tests (TOEFL/IELTS/CAE/CPE).

Responsibilities

• Take courses totaling at least 30 ECTS.
• Carry out high-quality research as described for AIMOS.
• Publish Academic work and disseminate popular science.
• Collaborate with AIMOS PhD candidates on fluid-structure interaction and AI.
• Participate in the SIMLab research group.
• Be flexible with duties as the project evolves.

Skills

Python programming

Machine learning

Data analysis

Communication

Education

Master's degree in mechanical engineering or computer science

Five-year Norwegian master's path (120 credits)

Admission to doctoral program requirements

Tools

Finite-element analysis

Job description

NTNU in Trondheim invites applications for a PhD Candidate position focused on AI-driven smart material characterisation and modelling within offshore structures. The role combines experimental work, physics-informed modelling, and machine learning to develop predictive material models for adaptive marine infrastructure.

The successful candidate will work in a leading, international research environment, contribute to academic publications, and participate in cross-disciplinary AIMOS activities