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transcriptomic readouts from cells - already produced, using diverse sequencing methods including long-read nanopore-sequencing with a protocol developed by the group - with of machine learning methods. We aim
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The Machine Learning for LIfe Science (MLLS) and the Cosmic DAWN Center invite applications for a PhD Fellow position starting in June 2024 or as soon as possible after that. The position is jointly
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department with strong research sections in the areas of Human-Centred Computing ; Algorithms and Complexity ; Machine Learning ; Natural Language Processing ; Software, Data, People, and Society ; Programming
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possibility for further extension. The successful candidate will engage in developing innovative biomechanical models and simulation techniques, integrating these with machine learning. This exciting
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, geometry processing, and a keen interest in applying machine learning techniques to solve these complex and critical medical challenges. The successful candidate for this postdoctoral position will be
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the clinical value of such approaches. The proposed project will follow a novel approach of quantifying and optimising plan quality in training of the deep learning model with direct prediction of machine
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collaboration with computer scientists and Industry, create new solutions and tools Define a wide range of storage and computing solutions to facilitate efficient data management and extraction Implement robust
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advanced data science and machine learning techniques. The start data is February 15, 2024, or after agreement. About CBMR Our researchers share a common purpose as they seek to transform the basic
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preparation. Ability to work effectively independently as well as collaboratively. Ability to learn and develop new skills related to specimen and data management. Good communication skills. Experience working
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candidates who work in the areas of visual encoding of data, interactions with data, graphical perception, combinations of machine learning and visualization, evaluations of visualizations, visualization in