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methods may include physical models, multivariable analysis, self-diagnosis, and AI algorithms. Given the importance of long operating times for underwater sensors, energy-efficient processing is central to
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other PhD projects within SFI Smart Ocean. These methods may include physical models, multivariable analysis, self-diagnosis, and AI algorithms. Given the importance of long operating times for underwater
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and evolutionary comparison software tools, making experience in bioinformatics, programming, and evolutionary comparison essential for this part of the project. Next, the candidate will verify
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further developed and supplemented by additional image-processing algorithms for studying liquid flow in real time. Development of experimental design and test rigs. Evaluation of accuracy in measurement
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of theoretical and applied IT programmes of study at all levels. Our subject areas include hardware, algorithms, visual computing, AI, databases, software engineering, information systems, learning
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Sustainable Energy AS. Duties of the position The technical work tasks concern: Development of smart algorithms and modules for load prediction and minimization of fuel, energy, and emissions for marine vessels
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of structures, facilitating a form-finding process driven by FEM analysis. Training deep learning algorithms to suggest multiple structural concepts tailored to specific boundary conditions. Expanding FEM
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primary focus of the project, but complementary research on parallel systems may be developed. The project will add an important evolutionary component to ongoing interdisciplinary research on Arctic
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, optimization algorithms, and machine learning techniques to tackle this challenging, interdisciplinary problem. As a PhD candidate with us, you will work to achieve your doctorate, and at the same time gain
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. Knowledge Graphs based on engine propeller combinator diagrams of the same vessels. Machine learning algorithms for data clustering and regressions of ship performance and navigation data sets as a part of