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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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labs framework. This studentship will include cohort-based training and activities, enabling students to gain wider skills and develop valuable personal and professional networks. Project Description
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sector at 70% of a TV-L E13 position, amounting to approximately €3,240 gross (€2,100 net) per month in the first year of employment. Housing arrangements are the responsibility of the PhD student, with
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remuneration package (including 39 days off a year and generous pension schemes). Be part of a diverse, inclusive and collaborative work culture with various staff networks and resources to support your
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including machine learning. This research will support the path to net zero flights and there will be opportunities to become involved in practical aspects of fuel system design and testing during their PhD
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infrastructure data, and building visual tools to support the interpretation and communication of results. The work has a strong foundation in systems and network research, with an emphasis on DNS protocol
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requirements. Build appropriate networks within the team. See full advert for full information. Education, qualifications and experience Essential MSc degree in a relevant discipline to the project: social
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effort at the intersection of machine learning and applied mechanics. The focus of this position is on extracting information about what a neural network has learnt in a symbolic and (human) interpretable
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and Their Human Host: Networks of Interaction” (www.funginet.de ) funded by the Deutsche Forschungsgemeinschaft and the consortium SynThera funded by the Carl Zeiss Foundation (www.synthera.eu/ ). We
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’, code ‘8440F’ in ‘Course Title’, blank in ‘Research Area’. Press ‘Search’, select ‘PhD Translational and Clinical Research (FT)’, and save selection. Either upload document or write into ‘Personal