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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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recent large-scale capabilities in physics. Reliability, exploring uncertainty quantification and robust inference in machine learning. Explainability, leveraging identifiability and unique recovery
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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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on conventional computing platforms such as GPUs, CPUs and TPUs. As language models become essential tools in society, there is a critical need to optimize their inference for edge and embedded systems
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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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; 1) tracking the historical transformation in medical and cultural representations of benzodiazepines and related substances in Europe 2) digital trading practices among dealers through online markets
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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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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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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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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