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Field
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. Without these guarantees, the algorithms will remain limited to experimental testbeds. The aim of this project is to address this limitation by combining the complexity of deep-learning control policies
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. Analysis of images will investigate the efficacy of manual digital approaches (e.g., Dot Dot Goose) and the development of a marine litter characterisation and quantification algorithm for automated analysis
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programming tools (e.g. OpenMP, MPI) Accelerator programming (e.g. CUDA, OpenCL, SYCL) Serial and parallel debugging and profiling Parallel numerical algorithms and libraries. System software stack
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conduct cutting-edge research on topics including, but not limited to: Complexity theory Quantum algorithms and complexity Sublinear algorithms Interactive proofs, PCPs, and zero-knowledge proofs
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samples. All computational methods and algorithms will be implemented as part of the python based MetaboLabPy platform (https://doi.org/10.3390/metabo15010048 , https://github.com/ludwigc/metabolabpy
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to shape—and create—new forms of AI material—algorithms, interfaces and embodiment—through the pursuit of answers to a complex question: in what ways can we make life better for all with interactive digital
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). You will develop an AI/machine learning model using spatial proteomics that can accurately differentiate CRS phenotypes. You will have an opportunity to apply the AI/machine learning algorithm to tissue
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research to investigate new optimisation algorithms for the refinement of single-particle and/or subtomogram data for cryo-EM structure determination. Main duties: • To undertake research aimed at aimed
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work with hands-on offloading algorithm design and development for IoT networks. The core responsibility is to build and validate edge-assisted offloading strategies, complete with software APIs, through
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processes that could be realised in neuromorphic hardware. The research will combine theoretical derivation and simulation-based validation, using mathematical modelling, algorithmic experimentation, and