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future autonomous instrument control and self-directed experimentation will be developed, recognizing the challenge presented by the integration of multiple complex systems. Coding and user interface
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materials systems at the molecular level with machine learning. The PhD Student will work with tumour sections to develop multiple instance learning and weak supervision / spatial transcriptomics models
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sensors, communicating over networks, to achieve complex functionalities, at both slow and fast timeframes, and at different safety criticalities. Future connectivity of the next generation of multiple
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Supervisors: Professor Richard Hague1 , Professor Chris Tuck1 , Dr Geoffrey Rivers1 (1 Faculty of Engineering) PhD project description: Inkjet printing allows multiple materials to be 3D-printed
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physical laws, or an implicit form of extra data examples collected from physical simulations or their ML surrogates. In medical domains, patient data is typically distributed across multiple hospitals
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project description: Inkjet printing allows multiple materials to be 3D-printed simultaneously, useful for printing functional devices. Discovering the interactions of these materials and how to leverage
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-time 65%) in the DFG-funded Integrated Research Training Group (RTG) Beyond Amphiphilicity – RTG 2670: Self-Organization of Soft Matter via Multiple Noncovalent Interactions . The position is funded from
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TV-L, part-time 65%) in the DFG-funded Integrated Research Training Group (RTG) Beyond Amphiphilicity – RTG 2670: Self-Organization of Soft Matter via Multiple Noncovalent Interactions . The position
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Physical Job Functions: Certain jobs at Duke University and Duke University Health System may include essential job functions that require specific physical and/or mental abilities. Additional information
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operation of autonomous systems in complex, real-world conditions. This PhD project aims to develop resilient Position, Navigation and Timing (PNT) systems for autonomous transport, addressing a critical