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Field
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optimisation algorithms to dynamically reconfigure the substation/distribution network settings to enhance the system efficiency. The optimisation algorithms will incorporate the uncertainties associated with
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algorithms, AI-driven applications and generative AI, exploring how legal mechanisms can prevent and remedy systemic biases that adversely impact LGBTQ+ individuals and which legal routes are available
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networks, making use of intelligent AI-driven control planes. The work will involve: i) (i) development of network probing algorithms for OSNR/GSNR measurement automation; (ii) development of adaptive ML and
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mapping using a team of highly mobile legged or legged-wheeled robotic platforms. The research will investigate advanced algorithms for multi-robot coordination, dynamic path optimization, and collaborative
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autonomous by embedding machine learning algorithms to search through different reaction parameters Person Specification Candidates should have been awarded, or expect to achieve, EITHER: A Bachelors degree in
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novel sensing approaches to combine with machine learning algorithms to solve real-world problems in food manufacturing. You will have sound knowledge in electronic engineering, embedded systems design
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, computer science, medicine, pharmacology, and physics. ISAS is a member of the Leibniz Association and is publicly funded by the Federal Republic of Germany and its federal states. At our location in
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looking for PhD Students in areas related to: Cybersecurity, Privacy and Cryptography Machine Learning and Data Science Efficient Algorithms and Foundations of Theoretical Computer Science Software
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Kyropoulou and will work in areas related to (Theoretical) Computer Science, Algorithmic Game Theory, and/or Fair Division with potential applications in Blockchain in the context of the EPSRC funded research
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to): Transparency and contestability in AI Accountability in algorithmic infrastructures Technical mechanisms for evaluating and auditing AI systems Governance of open-source and general-purpose AI models Data rights