22 algorithm-development-"Multiple"-"Simons-Foundation"-"Prof"-"UCL" positions in United Kingdom
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Regent College London, part of the Regent Group; | London, England | United Kingdom | 32 minutes ago
leading independent higher education provider, offering flexible and inclusive learning across multiple London campuses. We are student-focused, digitally forward, and committed to academic excellence
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Regent College London, part of the Regent Group; | London, England | United Kingdom | 32 minutes ago
leading independent higher education provider, offering flexible and inclusive learning across multiple London campuses. We are student-focused, digitally forward, and committed to academic excellence
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The KTP project will enable DeltaXignia to augment their Compare and Merge software capability by leveraging Artificial Intelligence (AI). Their current offer is built using mathematical algorithms
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demonstrated ability to work within a geographically distributed networks of collaboration Proven experience in developing and implementing machine learning models and algorithms, ideally in the healthcare
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demonstrated ability to work within a geographically distributed networks of collaboration Proven experience in developing and implementing machine learning models and algorithms, ideally in the healthcare
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-sensitive data augmentation and mining Manage multiple stakeholders, meet deadlines, and integrate project outputs into DeltaXignia’s workflows with training Balance technical development and commercial goals
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modes (e.g., HCCI) for net-zero fuels like hydrogen and ammonia. A key innovative pillar is the development of an AI-driven control strategy. Machine learning algorithms, including reinforcement learning
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. Expected outcomes include: development of novel algorithms that significantly improve predictive accuracy for equipment failure; creation of scalable monitoring systems that reduce operational costs
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for Multi-Agent Decision-Making, https://oceanerc.com ). This timely project will develop statistical and algorithmic foundations for systems involving multiple incentive-driven learning and decision-making
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methods to improve the deployment, adaptation capabilities and safety of robots and critical infrastructures. The developed algorithms will be evaluated on legged robots, wheel-based robots and under