PhD Candidate — AI for Offshore Structure Modelling

Norwegian University of Science and Technology (NTNU); Trondheim; On-site; NOK 496,000 - 606,000; Full time

Benefits offered by this job

Open and inclusive working environment

Career guidance during PhD

NTNU employee benefits and pension

Job summary

NTNU invites applications for a PhD Candidate position in Deep Learning enhanced FSI modelling of Multi-modular Floating Structures within the AIMOS project. You will work on coupling CFD–FEM simulations and develop physics-informed DL surrogates to accelerate analysis, uncertainty quantification, and digital twin capabilities.

The role involves coursework, collaboration with fellow PhD candidates, and publication of research results in high-quality journals.

Qualifications

• Master's degree in marine technology, civil/ mechanical eng, or applied mathematics or equivalent.
• Average grade of B or better on NTNU scale or demonstrated equivalent.
• Admission to NTNU PhD program is required.
• Very good English skills (written and spoken).

Responsibilities

• Take courses totaling at least 30 ECTS.
• Conduct coupled CFD–FEM simulations (e.g. STAR-CCM–ABAQUS) for wave-induced motions and loads.
• Develop deep learning surrogates for fast predictions of motions, stresses, and loads.
• Validate models using simulations, experiments, and field data.
• Analyze dynamics of modular floating structures and inter-module coupling.
• Collaborate with AIMOS researchers and publish results in journals.
• Present findings at conferences.

Skills

Strong English

Python

MATLAB

Education

Master's degree in marine technology, civil/mechanical eng or applied mathematics (5-year Norwegian program)

Tools

PyTorch

TensorFlow

STAR-CCM+ / ABAQUS

Job description

NTNU invites applications for a PhD Candidate position in Deep Learning enhanced FSI modelling of Multi-modular Floating Structures within the AIMOS project. You will work on coupling CFD–FEM simulations and develop physics-informed DL surrogates to accelerate analysis, uncertainty quantification, and digital twin capabilities.

The role involves coursework, collaboration with fellow PhD candidates, and publication of research results in high-quality journals.