Research Assistant in Probabilistic Design and Surrogate Modelling - DTU Wind

Would you like to contribute to the green energy transition by advancing the design and reliability of floating offshore wind systems? The Structural Integrity and Load assessment (SIL) section at DTU Wind and Energy Systems invites candidates to apply for a Research Assistant position focused on Reliability-Based Design Optimisation (RBDO) of mooring and platform systems for floating offshore wind.

We offer a stimulating role in an international and interdisciplinary environment that promotes academic excellence and impactful engineering solutions. You will work in a dynamic team committed to research excellence, with access to state-of-the-art research infrastructure and career development support.

Job description
You will be part of the SIL team contributing to:

• Risk and reliability engineering, probabilistic design and system-level optimisation
• Development and application of surrogate modelling and machine learning techniques for computationally efficient system analysis
• Formulation of component and system limit states for mooring and anchor systems
• Design and validation of RBDO frameworks combining uncertainty quantification and cost/environmental impact metrics
• Integration of RBDO into floating wind system simulations using high-fidelity numerical tools
• Dissemination of results through high-impact publications and contributions to the broader academic and industrial community

As a specialist, you are expected to be fluent in data science and analytics, with a solid knowledge of reliability engineering, uncertainty quantification, and optimisation strategies for offshore structural and mechanical components. You will contribute to improving asset integrity and enabling cost-effective, high-performance design solutions for floating wind mooring and platform systems.

This requires the development of numerical and surrogate models, integration and analysis of simulation and environmental data, formulation of limit states, and the creation of performance-driven KPIs. These elements will feed into a reliability-based design optimisation (RBDO) framework, which supports sustainable and cost-efficient system design under uncertainty. The work will be carried out in close collaboration with the SIL section and the broader consortium of the EU-funded TAILWIND project, leveraging high-fidelity simulations, experimental insights, and sustainability-driven design practices.

Your primary responsibilities will include:

• Assisting in the development of a surrogate-based RBDO tool for mooring-platform arrays, incorporating environmental and material uncertainties
• Performing numerical simulations and developing meta-models for floating offshore wind systems using tools such as OpenFAST, SIMA, or OrcaFlex
• Implementing optimisation routines (e.g. genetic algorithms, gradient-free methods) to identify cost-effective and reliable mooring system designs
• Supporting the demonstration of RBDO methods using representative case studies
• Contributing to publications, reports, and EU project deliverables
• Collaborating with partners in the TAILWIND project, including DTU, to ensure alignment with project objectives

The following qualifications are relevant to this post

Essential qualifications

• Educational Background: A master’s degree (or equivalent) in mechanical, marine, civil, offshore, or wind energy engineering, with a strong foundation in applied mathematics, structural mechanics, or reliability engineering.
• Numerical Modelling Skills: Experience in developing and applying numerical models for the analysis of structural or offshore systems, ideally including floating structures, moorings, or anchor systems.
• Data Analytics and Uncertainty Quantification: Competency in the application of probabilistic methods, statistical inference, or machine learning to support reliability analysis or design under uncertainty.
• Simulation and Coding: Proficiency in scientific programming (Python, MATLAB, or similar) for data analysis, surrogate model training, and numerical optimisation.
• Engineering Knowledge: Familiarity with structural design principles, limit states, and performance-based or reliability-based design.

Desired qualifications

• Reliability and Risk-Based Design: Experience in structural reliability methods (e.g. FORM, SORM, MCS), risk-based optimisation, or RBDO in engineering applications.
• Surrogate Modelling Techniques: Knowledge of surrogate modelling techniques (e.g. Kriging, Polynomial Chaos, Gaussian Processes, or ANN-based regressors) applied to complex engineering simulations.
• Optimisation Algorithms: Exposure to global optimisation techniques such as genetic algorithms, evoluti