Arttu Polojärvi
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Doctoral Researcher in Physics-Enhanced Machine Learning for Floating Offshore Wind Digital Twins Aalto University in Finland
Degree Level
PhD
Field of study
Computer Science
Funding
Funded by the Research Council of Finland; full-time salaried Doctoral Researcher position with an expected starting salary of approximately EUR 3,000 per month (gross), increasing with progress.
Deadline
Dec 29, 2026
Country
Finland
University
Aalto University
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About this position
Aalto University is seeking a Doctoral Researcher to work on physics-enhanced machine learning for floating offshore wind digital twins. The project sits at the intersection of marine hydrodynamics, offshore structural dynamics and data-driven modelling, with a focus on estimating and monitoring the behaviour of floating wind turbines in demanding sea conditions.
Floating wind turbines experience complex motions in six degrees of freedom under irregular waves and turbulent wind. Their mooring lines and other critical components are difficult to sense directly, so the project aims to develop digital twins that combine hydrodynamic knowledge with measurements from a small number of sensors. The goal is to infer unmeasured motions and loads, track structural condition, detect early degradation, and update predictions as new data arrive.
The position is based in the Marine and Arctic Technology research group at Aalto University’s Department of Energy and Mechanical Engineering. The group studies ships and offshore structures in waves, wind and ice, combining machine learning and data-driven methods with hydrodynamic theory, numerical simulation and model-scale experiments. The successful candidate will work closely with this team and with international academic collaborators.
The research themes include real-time estimation of wave-induced motions and mooring loads, physically consistent learning across changing sea states, and uncertainty-aware monitoring using probabilistic methods. The project specifically highlights model ideas such as Koopman operator theory and dynamic mode decomposition, and also refers to hydrodynamic simulations, potential-flow methods, CFD and coupled simulation tools such as OpenFAST.
This is a research-intensive doctoral opening in a multidisciplinary environment at Aalto’s Otaniemi campus in Espoo, where the work is framed around safer and more cost-effective floating wind farms and the indirect monitoring of hard-to-access components.
Funding details
Funded by the Research Council of Finland; full-time salaried Doctoral Researcher position with an expected starting salary of approximately EUR 3,000 per month (gross), increasing with progress.
What's required
A Master's degree completed by the start of the position is required. Suitable backgrounds include naval architecture, marine or ocean engineering, offshore engineering, mechanical, civil or aerospace engineering, applied mathematics, physics, computer science, or a related field. Applicants should have a strong foundation in linear algebra and in at least one of hydrodynamics or fluid mechanics, structural dynamics, dynamical systems, or signal processing. Good programming skills in Python or a similar language and a genuine interest in machine learning are needed, along with curiosity, independence, clear communication, and ability to work well in a team. Excellent English-language skills are required; Finnish-language skills are not required. Preferred experience includes marine/offshore hydrodynamics, OpenFAST, MoorDyn, WAMIT, dynamic mode decomposition, Koopman methods, Kalman filtering, Bayesian inference, Gaussian processes, uncertainty quantification, deep learning for time series, PyTorch or JAX, publications, or a strong Master's thesis.
How to apply
Submit your application through Aalto University’s recruitment system by 28 December 2026. Upload a single English PDF in the specified order: motivation letter, CV, referee contacts, Master's thesis description, transcripts, and research statement. Do not apply by email.
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Professors
Associate Professor at Aalto University
Finland
Research Interests
Rock Mechanics
Solid Mechanics
Cryosphere Science
Ice Science
Ice Mechanics
Make your next move.
Prepare your application
Review drafts and confirm requirements with the university.