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designed to meet multiple needs in marine biodiversity monitoring. The project aims to develop embedded novel deep learning and computer vision algorithms to extend the system’s capabilities to classify
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resiliency, and energy management algorithm development using MATLAB/Simulink for marine microgrid applications. Knowledge on control is highly preferred. Have experience and commitment to supervising student
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processes that use data-driven machine learning. Given the span of the IN-CYPHER programme, we are seeking multiple motivated research fellows. Unique in its scope, we are developing technologies that span
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processes that use data-driven machine learning. Given the span of the IN-CYPHER programme, we are seeking multiple motivated research fellows. Unique in its scope, we are developing technologies that span
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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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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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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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. Expected outcomes include: development of novel algorithms that significantly improve predictive accuracy for equipment failure; creation of scalable monitoring systems that reduce operational costs