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of Aberdeen to work on a project “Predicting Response in Triple Negative Breast Cancer Using Machine Learning”. We seek to appoint a creative and motivated individual to use machine learning (ML) to identify
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application of innovative Machine Learning (ML) frameworks to understand and predict the global hydrological cycle. The role will require bridging the gap between process-based physical modeling and scalable
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responsibilities will include: Explore innovative methods for food process optimization including the use of AI and machine-learning Develop and execute methods for characterizing and linking the texture and
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second in the UK for research power and first in England. About the role The project will be carried out at the Department of Computer Science, in the Machine Intelligence Lab (https
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transformations. Combining automated reaction discovery simulations, microkinetic modelling, machine learning, and global optimization, this project will develop a new and exciting route to addressing one
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phenomena and impulsive events in the solar atmosphere. Our approach includes the implementation of machine learning models for multiline full-Stokes inversions. About the person: The successful candidate
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/Computer Vision. The experience in diffusion models is a plus. Have a PhD degree in computer science/engineering or related disciplines. Knowledge of autonomous vehicles or cyber security will be
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machine learning, spatial audio and audio-visual AI into groundbreaking creative technology. About you We seek a talented Research Fellow to investigate generative audio AI technology for production
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-centric machine-learning frameworks for large-scale sequence-structure analysis and functional prediction. The role involves designing, implementing, and benchmarking computational models; developing and
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/drc/ ). About the role The role will contribute to on-going research at the UCL Hawkes Institute to develop advances in computational modelling of neurodegenerative disease, machine learning, and big