76 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:"-"The-Francis-Crick-Institute" positions at Ulster University
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Apply and key information This project is funded by: Department for the Economy (DfE) Summary CNC machining delivers high precision but is costly, rigid, and limited in adaptability. Robotic
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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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approaches often provide only limited insight into these effects. This project will use advanced computer simulation, informed by post-operative scans and patient movement data, to understand how variations in
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attention and comprehension in beginning readers. NPJ science of learning, 5(1), 1-10. Givan, P. (2025). Written Ministerial Statement to the Assembly Early Learning and Childcare measures 2025-26. https
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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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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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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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machine learning with spectral data to enable rapid, non-destructive detection of food adulteration and fraud. Machine learning combined with spectral data can play a vital role in combating food fraud by
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machine learning, Reliability Engineering & System Safety, Volume 264, Part A, December 2025, 111368 L. Deng, C. Shi, H. Li, M. Wan, F. Ren, Y. Hou, et al. Prediction of energy mass loss rate for biodiesel