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on human organ-based immunology with respect to allograft and tumor rejection and tolerance and mechanical extra-corporeal perfusion studies using tissue samples subjected to multiplex labeling, digital
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science or equivalent. A relevant master’s degree, or industrial experience, is desirable but not essential. English language requirements: Applicants must meet the minimum English language requirements
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valuable experience that qualifies you for a further career in higher education and research, in and outside academia. Your immediate leader will be the Head of the Acoustics Group About the project This PhD
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Research theme: Fluid Mechanics, Machine Learning, Ocean Waves, Ocean Environment, Renewable Energy, Nonlinear Systems How to apply: How many positions: 1 Funding will cover UK tuition fees and tax
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of sustainable aluminium production! Your immediate leader will be the Head of Department. Your supervisor will be Prof. Andreas Erbe. About the project You will delve into the mechanisms of surface treatments
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace - In Partnership with Rolls-Royce PhD
knowledge graphs for predictive insights. Design feedback mechanisms to deliver interpretable outputs such as alerts, recommendations, and confidence scores. Validate system robustness using real-world
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negative association of inhalation exposure to air pollution, the mechanisms of action to which air pollution impacts lung health are currently unclear. To elucidate this, using a physiologically relevant
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) Master degree required for PhD vacancy3) First and Master degree must be in Mechanical Engineering4) Experience with CFD simulations and Fluid Dynamics is a plus.5) Publications are preferred for PhD
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public organisations operate and coordinate themselves and their relations with private parties in the construction industry. This PhD project aims to experiment with unique shared service centres
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One Research Associate position exists in the data-driven mechanics Laboratory at the Department of Engineering. The role is to set up a machine learning framework to predict the plastic behaviour