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available in the Geometric Machine Learning Group at Harvard University, led by Prof. Melanie Weber. This role offers an opportunity to perform research on Riemannian Optimization. The ideal candidate has a
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of what microscopes can achieve. You will create and apply sophisticated algorithms, physics-based simulations, and machine learning models to process complex data from our cutting-edge imaging systems
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. Demonstrated skills in longitudinal modelling and data synthesis, including machine learning, regression and GLM. Demonstrated ability to undertake high quality academic research in the field of dementia
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: Erlangen Programme for AI” This is a 5-year programme supported by the EPSRC and is a collaboration of mathematicians and computer scientists at the University of Southampton, the University of Oxford (lead
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decision making, while you will be capable to apply machine learning and computational algorithms of social choice. This post is associated with following projects: Embedding EDI in the Distribution
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results. Preferred Qualifications: Experience with generative AI deep learning and active involvement in data science and machine learning projects. Experience in neural network architecture, cloud
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areas. Key Responsibilities: To independently undertake research in computer vision and machine learning. To produce research reports and/or publications as required by the funding body
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testing data Development of machine learning models for battery health assessment and remaining useful life prediction Job Requirements: PhD degree in Electrical Engineering or related subjects. Expert
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exciting project that will develop new approaches to handle missing data in statistical analyses based on machine learning methods. The Research Fellow will be based in the Department of Medical Statistics
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optimization of multi-modal LLMs. Investigate and implement methodologies to ensure AI authenticity, accountability, and the integrity of digital content. Develop and refine machine learning and deep learning