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Background in probabilistic methods Experience with the application of AI algorithms and probabilistic methods Good programming skills Personal characteristics To complete a doctoral degree (PhD), it is
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of study at all levels. Our subject areas include hardware, algorithms, visual computing, AI, databases, software engineering, information systems, learning technology, HCI, CSCW, IT operations and applied
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; mathematical modelling of cancer; probabilistic modelling and Bayesian inference, stochastic algorithms and simulation-based inference; causal inference and time-to-event analysis; and statistical machine
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detection and classification algorithms using measured and/or simulated data, such as current pulses from cable faults (breakdown), partial discharges and external noise. In addition to being part of
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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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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