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the challenge of time-consuming sideshaft testing. As a key member of the team, you will apply cutting-edge machine learning and deep learning techniques to dramatically reduce testing cycles. You will lead life
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conceptual background in cellular immunology. Interest in, and ability to, learn bioinformatics. To apply, please submit the following documents to Prof. Magdalena Plebanski (magdalena.plebanski@rmit.edu.au
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decisions related to turbine inspection and maintenance with a human expert [4, 5]. This can be based on a deep reinforcement learning framework, which interactively optimises key performance indicators in
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Sintorn, Professor in digital image processing, at the Department of Information Technology and conducted alongside researchers developing computational methods with a particular focus on deep learning and
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spectrum, in topics in virology and immunology, and currently specializes in computational biology focusing on developing methods and applications of deep learning for protein sequence and structure, as
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, within the Centre for Image Analysis at the Department of IT and conducted alongside researchers developing computational methods with a particular focus on deep learning and image analysis. The project
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Organisation Job description Are you passionate about combining the directed evolution of diverse biomolecules with deep learning approaches and contributing to the development of better (bio
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fees and include a tax-free stipend (£19,237 pa. currently), for a period of 3.5 years. The successful candidate will be supervised by Prof. Kurt Debattista and Dr. Thomas Bashford-Rogers
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that enables cohesive operation of the design and production system. This position is part of the EIC Pathfinder Project AM2PM: “Additive to Predictive Manufacturing for Multistorey Construction using Learning
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Matlab/Python/LabVIEW, with an added advantage of specific experience in popular deep learning frameworks like PyTorch and TensorFlow. Demonstrated capability in independent research, exemplified by