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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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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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relevant background in algorithms and/or database systems with a research-oriented master’s thesis. Good programming skills. Good written and oral English language skills. Your education must correspond to a
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to your work duties after employment. Required selection criteria You must have a professionally relevant background in algorithms, machine learning, database systems, or data mining, with a research
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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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, 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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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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selection criteria You must have a relevant background in algorithms and/or database systems with a research-oriented master’s thesis. Good programming skills. Good written and oral English language skills
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, and employ innovative design approaches and modelling techniques such as optimization algorithms. Specifically, your study will involve: Thermal Insulation Behaviour: The research will model how thermal