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Field
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The lab of professor Jesper Tegnér at KAUST has openings for three postdoctoral fellowships in Data-driven Machine Learning for unbiased Discovery of Generative Models with special reference
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uses. To investigate feature-level just-noticeable difference modelling for machines to facilitate assessment and optimization. To formulate a comprehensive visual feature codec for machine uses
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in this position will conduct/lead applied as well as fundamental research in physics-informed Artificial Intelligence (AI) and Machine Learning (ML) methodologies enabling digital twin functionalities
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experience Evidence of experience with public-patient involvement and stakeholder engagement Track record in scientific publication and dissemination Experience with Machine Learning Algorithms and Artificial
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): Research experience with literature review, data collection from different stakeholder groups. Advanced skills in using Stata or R. Machine Learning experience Artificial Intelligence Excellent written, oral
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aspects of predictive modeling. A Ph.D. in bioinformatics, genetics, statistics, computer science, or a comparably quantitative discipline (e.g., mathematics, physics), or comparable research experience
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methodologies or models from an engineering perspective, as well as scientific studies focused on understanding deep learning. This includes the development of novel applications of artificial intelligence
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challenges from low carbon shipping and sustainable fuels to solar power technologies and advanced brain models. Learn more at https://mecheng.ucl.ac.uk . Within this dynamic environment, the Moazen Lab is
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algorithms and methods for adaptive and personalised feedback, modelling learning behaviours with sequence and deep learning methods, and generating interpretable insights through novel analytics and
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can be leveraged to accelerate learning from both classical and quantum data. The project will develop rigorous theoretical frameworks to understand key properties of quantum machine learning models