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for three consecutive periods (2014-2018 and 2018-2022 and 2023-2026). ICN2 comprises 19 Research Groups, 7 Technical Development and Support Units and Facilities, and 2 Research Platforms, covering different
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, or erroneous data, Data cleaning and generation, Development of enhanced loss functions and information-theoretic methods for optimized data analysis, Machine learning-based image segmentation of tomographic
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need to understand what plasma conditions are optimal for producing light from plasma, but also we need to understand how such plasma should be generated, and what laser technology should be used. You
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to ensure optimal tissue concentrations during surgery. The PhD student will utilise national and international arthroplasty registry data, adapt in vitro diagnostic tools such as the Minimum Biofilm
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material layers that can be optimized to specific battery chemistries and flow phenomena from the microscale up. The developed technologies will be validated in half-cells and full working batteries
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affordable and durable long-duration energy storage. The approach is to use hierarchical structures, i.e. complex material layers that can be optimized to specific battery chemistries and flow phenomena from
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sanitation industries. Working with our established industry partners, you'll implement your innovations in real operational environments, seeing your research make tangible difference while building
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on pre-existing/collected data using Supervised/unsupervised fine tuning of the existing models (e.g., Retrieval-Augmented Generation, Low-Rank Adaptation, Direct Preference Optimization, Reinforcement
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explore promising synergies in landscape services provision and analyse how to optimally align this with the demand for these services across the urban-rural region. In doing so, you will contribute
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the sensory and functional qualities of natural seafood. This PhD project is expected to develop technology for cultivated seafood, by selecting the optimal cell source, developing a continuous cell line and