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Field
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sensors - if we can control and tune their properties. You will develop and use top-of-the-line machine learning models to predict the sensor response of these materials under realistic conditions
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candidate for a full-time (100%) PhD position for 3 years. You will join the research group Power Electronics and Electrical Machines (PEM) at IEL, where we foster an open, inclusive, and collaborative
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closely related discipline). You have a strong interest in AI/machine learning, data mining, regression analysis, responsible AI, causal inference, and programming (R/Python, SQL). Moreover, you are driven
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profiles. Identification of molecular signatures in platelet disorders and bleeding disorders of unknown cause (BDUC). Development of machine learning models for patient stratification. Integration
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are applied in real-world settings characterized by large-scale networks, stochastic demand, operational disruptions, and complex constraints. A central research question is how machine learning can be
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machine learning and conventional optimisation techniques. 2. To design and optimise magnonic primitives for wave-based neuromorphic computing, including programmable devices enabling nonlinear activation
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deployed. The research will therefore combine qualitative fieldwork with computational modelling to develop approaches that operate under uncertainty, adapt as the landscape changes, and generalise beyond
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to quality, integrity, creativity and cooperation. You have a profound knowledge of wireless communications, networking, and signal processing. You have at least intermediate knowledge of machine learning
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PhD Candidate to conduct research on Artificial Intelligence for managing Shipbuilding Supply Chains
for the position. Preferred selection criteria Experience with machine learning or other relevant AI technologies Scandinavian language skills Previous experience from industry or research in engineer-to-order
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establish a digital route to quantify the segregation behaviour of residual elements at austenite/austenite grain boundaries through atomic-scale simulations, using modern machine learning techniques and in