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performance. This PhD project aims to develop a data-driven framework for graphene aerogel design by integrating structured experimental Design of Experiments (DoE) with machine learning (ML). The student will
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collaborative initiative between the Electrical and Computer Engineering and Computer Science Departments. A complementary faculty search is being conducted by the Electrical and Computer Engineering Department
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description Integreat - the Norwegian Centre for Knowledge-driven Machine Learning at the University of Oslo invites applications for a doctoral research fellowship. The PhD candidate will work at the
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both theory and concrete tools to design systems that learn, reason, and act in the real world based on a seamless combination of data, mathematical models, and algorithms. Our research integrates
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Engineering » Computer engineering Engineering » Electrical engineering Engineering » Knowledge engineering Researcher Profile First Stage Researcher (R1) Application Deadline 29 May 2026 - 23:59 (UTC) Country
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relevant background within Learning Technologies, Interaction Design, Human-Computer Interaction (HCI), Participatory Design (PD) or alike. You must have a Master's degree in Computer
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-physics modelling of power electronic systems and components, with special focus of magnetic components, Incorporating physics-driven machine learning approaches in power electronics design, Incorporating
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the Advanced Materials Group (Department of Control Engineering, Faculty of Electrical Engineering, Czech Technical University in Prague), to work on the computational materials science work “Diamond-based
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, Interaction Design, Human-Computer Interaction (HCI), Participatory Design (PD) or alike. You must have a Master's degree in Computer Science, Information Science or equivalent (with a focus on learning
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, silicon-proven AI/ML accelerator for transmitter error correction (digital predistortion/calibration). Your work will sit at the intersection of machine learning, DSP, and digital IC design, and you will