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Call :    Sustainable Blue Call 2023 on "The way forward: a thriving sustainable blue economy for a brighter future"
Looking for :    a project to join
Prof. Dr. Merten Stender
Mr.
merten.stender@tu-berlin.de
Germany
TU Berlin
Cyber-Physical Systems
https://www.tu.berlin/
https://www.researchgate.net/profile/Merten-Stender

a project to join
Looking for a project in Machine Learning, Wave Forecasting, Complex Systems, System Identification, Nonlinear Dynamics, Deep Learning, Digital Twins, Cyber-Physical Systems

We are developing digital twins for structures and fluids, combining physics-based methods with data-driven techniques. Relevant projects are ocean wave forecasting (https://doi.org/10.2139/ssrn.4349150), machine learning solutions of PDEs (https://dx.doi.org/10.2139/ssrn.4053304), data-driven structural dynamics (https://doi.org/10.1016/j.ymssp.2020.107181), wind farms (https://doi.org/10.1088/1742-6596/1934/1/012022) and overall nonlinear complex dynamics. We are experienced in writing grants, coordinating projects, and finally winning grants.
PA2 - Development of offshore marine multi-use infrastructures to support the blue economy
PA5 - Digital Twin of the Ocean (DTO) test use cases at EU sea-basins and the Atlantic Ocean
We are looking for a project to bring in our expertise in data-driven behavior prediction and data assimilation in the field of wave dynamics, structural dynamics, and complexity. For example, we are able to make accurate flow and wave predictions over some time period for decision support (seakeeping operations), build digital twins for multi-physic situations, fuse multiple data sources for making predictions, considering multiple time and length scales, identify systems from (real) measurement data. Ideally, partners would bring in an actual application scenario and some real-world measurement data. We bring much experience in writing successful grant applications.
machine learning, artificial intelligence, deep learning, forecasting, waves, structures, vibrations, data assimilation, system identification, complexity
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