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data are needed to enhance our understanding of sources, pathways and impact of litter. Cefas is developing a visible light (VL) deep learning (DL) algorithm and collected a large 89 litter category
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in basic control-flow analysis. Process mining, as it stands today, is primarily based on computational techniques and algorithms to analyze and optimize processes. Methods such as process discovery
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. About your role: Develop improved physical models of the image formation process in holographic X-ray imaging Design and implement reconstruction algorithms for handling large-scale tomographic data from
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of federated algorithms that enable fair management of the advantages and duties of energy communities in which vulnerable people live. The automatic generation of data models using AI technology will enable
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qualifications will be considered. Experience of using machine learning algorithms and toolsets, ideally in a research context. Strong programming skills (e.g., Python, Java, C++) An interest in physiological
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implementation of security provisioning algorithm in DFL environment Where to apply Website https://sede.uvigo.gal/public/catalog-detail/28364578 Requirements Research FieldEngineering » Communication
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creating robust, low cost, and real-time edge-AI algorithms capable of accurately classifying diverse marine species and debris under complex and dynamic underwater conditions. The demand for such a low-cost
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-terminal antennas and beamforming operating in FR1 bands and future FR-2, enabling robust terrestrial–satellite integration for safety-critical air mobility services. To develop AI-based algorithms
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for photocatalytic degradation and H2 production.Designing multi-objective optimization algorithms to maximize environmental and economic performance. Your RoleAs a PhD researcher, you will: Build and validate hybrid
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the fundamental limits of quantum error correction (QEC) while concurrently advancing efficient decoding algorithms for quantum error-correcting codes in the near-term, noisy intermediate-scale quantum (NISQ) era