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Field
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funding affect the subsequent performance of firms and scientists, in terms of outputs such as the number of papers, products, patents, etc. (Can an optimal applicant template be developed by training
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of advanced computational techniques. This research will integrate power system modelling, optimisation algorithms, and artificial intelligence (AI) techniques to develop an innovative framework for strategic
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learning and data analysis experts. The main tasks include the analysis of complex biomedical data using modern AI methods, as well as the development of novel machine and deep learning algorithms
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safety. You will work on developing control algorithms all the way to performance assessment in test vehicles. The project combines theoretical aspects of control algorithms, experimental design, and
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consumption while guaranteeing optimal power production. You will work on the cutting edge of both wind energy and machine learning, two of the fastest growing scientific disciplines, to develop graph-based
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through theory and simulation and/or experimental design and testing; developing new image reconstruction algorithms for providing more information with less radiation; and applying our techniques
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input needs, accompanied by a boost in algorithmic development, e.g., multi-modal learning, transfer learning, federate learning, and knowledge embedding, etc. However, a significant motivation of
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microscopy and atom probe tomography will be prepared. Finally, you will merge the images by means of deep learning algorithms. Your tasks in detail Development of the experimental protocol for the imaging
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algorithms. Graph Neural Networks. The candidate is expected to hold a relevant MSc degree in Computer Science, Data Science, Physics, (Applied) Mathematics, Computational Statistics or another field
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control laws into Trent gas turbine engines and developed algorithms monitoring fleets of 100s of engines flying all around the world. During the PhD, you will have the opportunity to deeply engage with