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into company valuations. You'll apply cutting-edge machine learning techniques (transformer models, causal forests, double machine learning) to understand which aspects of patent language predict valuable
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interactions, and mobility are collected). Machine Learning models will be trained to infer fatigue in real time, triggering adaptive prompts, such as suggesting micro-breaks. Expected outcomes include a sensing
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for stress-testing. Training spans multi-agent reinforcement learning, evolutionary computation, adversarial machine learning, game-theoretic modeling, and financial crime compliance. You will design agent
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to compliance professionals for validation studies. Training spans advanced NLP (transformer fine-tuning), financial crime typologies, privacy-preserving machine learning, and product-oriented development. You
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, biodiversity monitoring, and climate resilience. The work supports strategic priorities in Environmental Sciences, Software/Cyber. PhD researchers will explore how AI-driven Earth observation, computer vision
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. The successful candidate will join the Intelligent Systems Research Centre (ISRC) at Ulster University’s Magee campus, working with experienced researchers in machine learning and cognitive analytics
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in exercise and dance interventions. Build and evaluate AI / machine learning models using labelled, collected multimodal data to classify motor and non-motor symptoms, identify digital biomarkers
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vast amounts of rich, complex data, unlocking their insights requires cutting-edge AI and machine learning (ML) techniques. Meanwhile, although artificial neural networks (ANNs) have powered recent AI
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and machine learning techniques for the design of superconducting qubits, one of the leading qubit modalities used in today’s quantum computers. The optimal design of superconducting qubits is a highly
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artificial intelligence to learn from data and make decisions. However, the real world is always changing. Brain signals vary over time, and machines wear out or behave differently as they age. As a result