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Field
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validate digital-twin and optimization methods for electrolysis systems, working both independently and collaboratively with the group and with academic and industrial partners. In particular, you will
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will operate at the intersection of computer vision, micro-electronics analysis, and hardware security, and will work under the supervision of researchers within the Department of Intelligent Systems
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the development of novel volumetric representations and computational imaging methods for paired visible-light and X-ray images, as well as extending computer vision techniques from the visible-light RGB
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sensors capable of characterizing the dynamic state of buildings and their inhabitants. By combining multi-microphone acquisition techniques with state-of-the-art machine learning methods, acoustic sensing
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skills: You have experience with, (or affinity for) programming and implementing experimental tasks and procedures. This includes, for example, developing, adapting or running computer-based experiments
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diagnosis methods that combine analytical approaches based on drivetrain physical properties with AI-driven data analysis techniques to enhance the accuracy and effectiveness of fault detection and
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the position. Your work tasks You will develop and validate digital-twin and optimization methods for electrolysis systems, working both independently and collaboratively with the group and with academic and
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Technology » Computer technology Technology » Communication technology Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 4 Oct 2026 - 23:59 (Europe/Oslo) Country
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be to develop wireless sensing and communication methods that are designed together with AI-based inference, rather than treating connectivity as a separate layer. Particular attention will be given
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are looking for candidates interested in developing new machine learning methods for medical image analysis, with a particular focus in anomaly detection and unsupervised learning. In this position, you will