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Your profile: A completed degree (Diplom or Master) in Computer Science or related fields Excellent knowledge of software security and machine learning Very good command of English Please include a
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, UAEs, GANs, diffusion models/flow matching, and Gaussian processes for realistic load, generation and voltage time series. You will embed physical constraints into generation: power-flow consistency
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& Control, Cyber-Physical Modeling, and Autonomous Building Operation, you will lead the mission to transform buildings into active, efficient entities within wider energy ecosystems. Your primary tasks will
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techniques for inspection of microfabricated devices. MATLAB, Python, LabVIEW or similar tools for automated experimental control and data acquisition. Experience working with external microfabrication
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monitoring and control. In this context, generative AI and foundation models offer promising approaches for developing trustworthy and scalable energy applications, including the generation of synthetic yet
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on temporal data (predictive maintenance, sensory processing for robot control, etc), in which efficient on-device processing is crucial. We are looking for a highly motivated PhD candidate with an interest in
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will find more information about working at NTNU and the application process here. ... (Video unable to load from YouTube. Accept cookie and refresh page to watch video, or click here to open video
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knowledge for a better world. You will find more information about working at NTNU and the application process here. About the position We have a vacancy for a PhD candidate in machine learning
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organisms and determine the environmental factors controlling their activity under future conditions. Ultimately, the project will improve our understanding of anaerobic microbial food webs in Antarctic
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four-generation family trees with financial information derived from probate inventories. The main phase of data collection has now been completed, and the project is entering a stage of data processing