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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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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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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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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
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learning, deep learning and relevant software framework (R and Python) is highly desired. Very good oral and written communication skills in English are required. Emphasis will also be given on personal
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Applications are invited for a position in the rapidly expanding data analytics run by Prof Adam Dubis. The main focus of the team is to develop deep learning tools for prediction of disease progression
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. The specific research areas we will explore are + Adaptive scientific deep learning methods for mathematical physics problems governed by partial differential equations (domain decomposition, adaptive quadrature
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learning’ approaches (such as Deep CNN’s) and ‘unsupervised learning’ approaches (such as reinforcement based learning and generative adversarial networks). Some of the main problems with Second Wave AI