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more information for a given experimental budget. Efficient active learning depends on the careful co-design of experiments and inference algorithms. You will explore topics such as how to elicit
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derived use cases by focusing on one or more of the following topics in their PhD project: Training and inference of ML models on GPU clusters. Method development for scalable and green AI. Use cases in
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with an excellent GPA or a proven track-record of relevant research will be considered for an interview. Approval and Enrolment The scholarship for the PhD degree is subject to academic approval, and the
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in teaching (preferably in science, science communication, or higher education) experience with immersive technologies (e.g., XR/VR/AR) and/or multimodal data analysis (e.g., eye-tracking, gesture
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technologies that support elderly individuals living at home, advancing smart healthcare solutions with the goal of enabling broader applications in remote monitoring, home-based health tracking, and
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to the requirements for learning or inference by various AI modules deployed in the devices and the network. This line of work will investigate the fundamental tradeoffs among latency, accuracy, and energy efficiency
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teaching track record and portfolio. Targeting aberrant biomolecular condensates in the PSD The five key proteins present in the PSD (PSD-95, SynGAP, Homer, Shank and GKAP) are important for normal brain
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expression and developability. Propose and validate optimization tools for performing (Bayesian) design of experiments. System validation and iterative refinement based on empirical data. Test and refine
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be integrated in the Kloverpris lab as part of NIVI-R. Project description The PhD project will focus on tracking the immune and epithelial changes in the respiratory tract from a human clinical trial
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track record of national and international research projects. We are particularly interested in candidates with experience in the use of sensory evaluation tools as well as in chemical and microbial