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, evaluating, and fine-tuning machine learning models (e.g. deep neural networks) to segment underwater scenes and classify anomalies. The work will explore the use of virtual environments and synthetic datasets
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efforts to contribute to safer marine operations, we actively explore possibilities to utilize both numerical and machine learning methods to enhance the accuracy and resolution of metocean forecasts. About
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modeling of dynamic systems. Experience with machine learning or AI methods applied to robotics (e.g. reinforcement learning for control, or data-driven modeling) is a plus, especially if applied in
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open up exciting career opportunities? Are you interested in cable technology and condition monitoring and do you have a strong competence in signal processing and machine learning? As a PhD candidate
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. The underwater acoustic communication technologies will help. The school is focusing on research in AI/machine learning and signal processing which are the research areas in this proposed project. We have
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the PhD candidate may include (non-)linear inverse load estimation and data-driven/machine learning techniques that rely on physics-informed guidance for improved robustness. A key task will be to quantify
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relevant experience in the development and deployment of machine/deep learning models as well as the use of remote sensing data You must have relevant experience in the development of hydrodynamic and water
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or pregnancy-related conditions, age, creed, religion, actual or perceived disability (including persons associated with such a person), arrest and/or conviction record, military or veteran status, sexual
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project management skills. Candidates with strong skillset, including familiarity with structural health monitoring, computer vision and machine learning are desired for this project. Must be eligible