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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 25 days ago
deep learning theory and practice. Applicants with expertise in probabilistic modelling, approximate inference, deep learning, or Bayesian optimisation are encouraged to apply. Interpretable Machine
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machine learning models with respect to accuracy and uncertainty quantification. - Developing software to implement the goals stated above (most likely in Python). - Disseminating results
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journals. Internal knowledge transfer, and collaborative research efforts. Develop and evaluate AI models, including, but not limited to, those for Multi-Agent Systems, Multi-Agent Reinforcement Learning
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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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), one method of SCI, poses on the Earth system. Specifically, the postdoctoral scholar will analyze climate model simulations to quantify the impacts and risks of SAI on climate extremes such as droughts
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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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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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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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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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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