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the area of fundamental modelling with molecular dynamic and ice/substrate interface bonding analysis. The main aspects of the research fellow role include conducting icing experiments in the Cranfield Icing
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modelling and spatial mapping approaches to join our team of academics working on decision support systems to improve schistosomiasis preparedness and control during development of water management
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the airframe can have a notable impact on both systems and affect the in-flight fuel burn. The research within this post will develop computational models of the coupled system which will be used to evaluate
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to assess potato dormancy break, including: data collection, processing, AI model development and classification accuracy assessment. Involved in supporting an electrophysiology-based machine learning model
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applicants will be required to undergo a DBS Criminal Record check and provide a significant work reference history. At Cranfield, we value Diversity and Inclusion, and aim to create and maintain a culture in
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for aircraft systems. You will integrate heterogeneous data streams (from multiple sources, such as sensory, physics model, etc.), flag impending failures, pinpoint and trace back the origin of system faults
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and Bioinformatics using Python and R and practical experience of applying these skills and techniques in Machine Learning models and software development. High level analytical capability is essential