PhD Candidate in Deep Learning Enhanced FSI analysis of Modular Floating Structures

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

About The Position

Would you like to work at the intersection of artificial intelligence and offshore engineering, contributing to the next generation of intelligent floating structures? The Department of Marine Technology at NTNU has a vacancy for a PhD Candidate in Deep Learning enhanced FSI modelling of Multi-modular Floating Structures. The position is part of the AIMOS project (Artificial Intelligence for Multiscale Offshore Structures), which develops integrated AI methods for the analysis, design, monitoring, and control of floating offshore systems.

Multi-modular floating structures are emerging as a promising solution for scalable offshore applications where conventional fixed-bottom systems are impractical. These systems consist of multiple interconnected modules subjected to waves, current, and mooring loads, resulting in highly nonlinear coupled hydrodynamic and structural behaviors. High-fidelity CFD-FEA based FSI simulations provide valuable insight but remain computationally expensive for large design spaces, long-duration simulations, and real-time applications. This PhD project will develop physics-informed deep learning and surrogate modelling approaches to accelerate simulation, uncertainty quantification, structural response prediction, and digital twin capabilities using data generated from high fidelity CFD-FEA simulations, experiments and field measurements. The position offers the opportunity to work on cutting-edge research with strong relevance to offshore renewable energy and marine infrastructure.

Your immediate leader will be the Head of Department.

Duties of the position

• Take courses in relevant technical topics, comprising a minimum of 30 ECTS.
• Conduct coupled CFD–FEM (e.g. STARCCM-ABAQUS) simulation of wave-induced motions, connector loads, mooring loads and structural deformation. This may be extended to include potential flow theory based modelling as well.
• Develop deep learning surrogate models for fast prediction of motions, stresses, and loads
• Validate the deep learning model using numerical simulations, experiments data, and field measurements.
• Analyse the dynamic behavior of modular floating structures, including connector loads, load redistribution, resonance effects, inter-module coupling, and response under varying sea states.
• Collaborate with other PhD candidates in AIMOS project, working with intelligent control and smart materials.
• Write scientific articles in high quality journals and present results in conferences

Be prepared for changes to your work duties after employment.

Required Selection Criteria

• You must have a relevant master’s degree in marine technology, civil engineering, mechanical engineering, applied mathematics or equivalent. Your course of study must correspond to a five-year Norwegian course, where 120 credits have been obtained at master's level.
• You must have a strong academic background from your previous studies and have an average grade from your Master's degree study, or equivalent education, which is equal to B or better compared to NTNU's grading scale. If you do not have letter grades from previous studies, you must have an equally good academic foundation. If you have a weaker grade background, you may be considered if you can document that you are particularly suitable for a PhD education.
• You must meet the requirements for admission to the faculty's doctoral program(https://www.ntnu.edu/studies/phiv)
• Very good English skills (both written and spoken)
• Applicants from non-English speaking countries outside Europe must present an official language test report. The acceptable tests are TOEFL, IELTS, and Cambridge Certificate in Advanced English (CAE) or Cambridge Certificate of Proficiency in English (CPE). Minimum scores are:
  • TOEFL: 600 / writing 4.5 (paper-based) or 92 / writing 22 (internet-based)
  • IELTS: 6.5, with no section lower than 5.5 (only Academic IELTS test accepted)
  • CAE/CPE: grade B or A.

Preferred Selection Criteria

• Strong background in marine, offshore, or structural engineering disciplines, including structural mechanics, hydrodynamics and machine learning
• Strong programming skills in Python and/or MATLAB
• Experience with scientific computing, CFD/FEM software, potential flow theory or machine learning frameworks (e.g., PyTorch, TensorFlow).
• Strong written and oral communication skills in English.

Personal characteristics

• Highly motivated and interest-driven to explore complex scientific and engineering problems
• Work independently and in structured way, as well as collaboratively within an interdisciplinary team.
• Communicate effectively and present and discuss your research with other professionals
• Get involved and contribute constructively with feedback
• Work constructively under pressure or in the face of adversity
• Be flexible and open to adjusting the plan for the project as needed

We offer

• An exciting job with an important mission in society
• Developing tasks in a strong and international professional environment
• Career guidance and follow-up during the PhD period
• Open and inclusive working environment with committed colleagues
• Working capital that can be used to implement the project
• Favourable terms as a member of the Norwegian Public Service Pension Fund (SPK)

As a PhD Candidate at NTNU, you will have access to employee benefits.

Diversity

Diversity is a strength, and at NTNU we aim to be an employer that reflects the diversity in society and that makes use of the potential of the population's collective skills. Our vision is Knowledge for a better world and our values are creative, critical, constructive and respectful. We believe that an organization that is equal, diverse and gender-balanced is essential for us to achieve our goals. We strive to attract employees with different skills, life experiences and perspectives to contribute to even better problem solving of our societal mission in research and education.

At NTNU we want to increase the proportion of women in scientific positions. We have a number of measures to promote equality.

Salary and conditions

In the position of PhD Candidate, code 1017, your gross salary will normally be NOK 550 800,- per annum depending on qualifications and seniority. A 2% statutory contribution to the State Pension Fund is deducted from the salary.

The employment period is 3 years for the doctoral work in addition to 4 months/year of teaching assistance.

For employment as a PhD Candidate, it is a prerequisite that you gain admission to the PhD programme in Engineering within three months of your employment contract start date, and that you participate in an organized doctoral programme throughout the period of employment.

As an employee at NTNU, it is important that you keep yourself up to date with academic and organizational changes and adapt to them.

For the necessary professional and social interaction, it is a prerequisite that you are physically present and available to the institution on a daily basis.

The appointment is carried out in accordance with the principles of the State Employees Act, and Export control (legislation that regulates the export of knowledge, technology and services). Candidates who, after assessment of the application and attachments, are considered to be in conflict with the criteria in the latter act, will not be able to be employed.

About The Application

The attachments (including a description of your scientific work) must accompany the application as these documents form the basis of the application assessment. The documents must be in Norwegian/a Scandinavian language or English.

The Application Must Include

• Transcripts and diplomas for Bachelor's and Master's degrees
• CV
• Copy of Master's thesis. If you have recently submitted your Master's thesis, you can attach a draft of the thesis. Documentation of a completed Master's degree must be presented before taking up the position.
• Project outline containing proposals for an overall description of research questions, theoretical perspectives, methodological design for the project and progress plan (maximum 1500 words/4 pages)