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capacity, or designing innovative care models that balance quality and efficiency. Using data-driven methods and quantitative modelling (e.g., simulation, optimization), you will develop practical solutions
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biological properties of the RWM. o Develop numerical and in-vitro models to investigate drug delivery. o Employ microfluidics technology to identify safe and minimally invasive methods for intra-cochlear
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sources. Such modelling provides essential tools for designing low-carbon, efficient, and adaptive energy infrastructures at local level. By leveraging data-driven methods, cities can devise strategies
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energy system modelling is critical for reducing emissions, enhancing resilience, and integrating high shares of renewable energy sources. Such modelling provides essential tools for designing low-carbon
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faces. At the same time, reinforcement learning is a key technology in artificial intelligence and machine learning that set various state-of-the-art results. In the Reinforcement Learning Lab
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, OntoGPT, LangChain, etc) for automated data recoding. - Prompt and agentic workflow engineering: devise and implement best practices for improving language model performance in data extraction and ontology
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inference. This framework comprises three main components: causal discovery, identification analysis, and experiment design. The causal discovery part learns a causal model of the environment from
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new data-driven hierarchical models. The main novelty is to represent vertically varying velocity profiles using an extended set of equations, resulting in so-called moment models. The whole project
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supporting data-driven methods and AI models in plant science. The project is part of the CropXR program, a highly collaborative 10-year national initiative of universities and industry, with a mission to grow
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platform for metal AM parts; 2)develop and perform advanced data-processing techniques (e.g., data-driven modeling with embedded nonlinear dynamics) for vibrational feature extraction; 3)conduct quality