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to achieve, at least a 2.1 honours degree or a master’s in a relevant science or engineering related discipline. Applicants should have strong background in Machine Learning and Deep Learning. To apply, please
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are developed, modelled and controlled. You will create novel adaptative, physics-informed models that tightly integrate thermo-fluid dynamic laws, deep learning neural networks, and experimental data. A key
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multidisciplinary research in energy markets, optimization, game theory, and machine learning. Our team of 13 members (link ), from 10 different nationalities, values diversity and includes experts from a range of
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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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data analysis, programming, and biology. You will be part of a collaborative research team with deep experimental and analytical expertise, with access to advanced tumor models and state-of-the-art
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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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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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, 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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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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the accurate prediction of reaction enthalpies and activation free energies for all relevant intermediates. In this project, a deep learning and generative design toolchain will be developed resulting in an ML