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Field
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PhD working with Prof. Hendrik Blockeel and/or Prof. Jesse Davis on analysis of time series data. The goal is to develop algorithms for detecting anomalies, discovering “motifs” (repeating patterns
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development of AI and avoiding its misuse. These issues might be exacerbated by the lack of formal guarantees in explaining the behavior of AI systems in terms of human-interpretable, high-level concepts. While
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flow reconstruction, enabling both real-time coarse diagnostics and high-fidelity offline velocity field estimation. Developing reinforcement learning (RL) algorithms for a multi-agent robotics system
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Multi-omics data integration and workflow improvement Development and application of machine learning-based algorithms for the identification of antibiotics-associated proteins and antimicrobial
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group of the University of A Coruña. The hired person must collaborate in the application of techniques based on computational grammars to the development of green natural language processing algorithms
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operations. The PhD will develop and implement artificial intelligence and data-driven methods for early anomaly detection, root cause diagnosis, and failure prediction on industrial systems, leveraging
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Contribute to the development of new algorithms and methods for the efficient analysis of large-scale omics datasets Participate in workflow automation and management using systems such as Snakemake
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therefore paramount, but traditional simulations are plagued by the same slow relaxational dynamics. Through collaboration across Engineering, Statistics and Chemistry, this project will develop state
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(algorithms), and statistics. During this project, you will develop new methods to construct phylogenetic networks and generalize mathematical frameworks of phylogenetic network classes to tackle related
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(Edge AI) enables deploying AI algorithms and models directly on edge devices. However, AI workloads demand high performance processing, large scale data handling, and specialized hardware accelerators