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This is an exciting PhD opportunity to develop innovative AI and computer vision tools to automate the identification and monitoring of UK pollinators from images and videos. Working at
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.) Application procedure: Please submit by email to Professor Adrian Smith at a.m.smith@sussex.ac.uk (via the 'Apply' button above) and as one pdf file an application including: A statement of interest that
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transmission electron microscopy, X-ray diffraction and, where necessary, synchrotron X-ray diffraction, nano-secondary ion mass spectroscopy, and 3D atom probe tomography. The data acquired will be used
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direct measurement of the crystal structures of the phases present and is sensitive to low volume fractions. Ancillary microstructural examination using electron microscopy will confirm the phase
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status in the UK). Application procedure: Please submit by email to Professor Dimitra Petrakaki at d.petrakaki@sussex.ac.uk (via the 'Apply' button above) and as one pdf file an application including: A
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include training in computer science, human-computer interaction, etc. A Master’s degree is desirable. In addition, candidates for whom English is a second language should meet the University’s minimum
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intrinsically, offering new solutions for photobatteries that operate more independently of fluctuating light, while offering new slutions for mobile or wearable (micro)electronics, and for light-information
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. You are encouraged to contact potential supervisors by email to discuss project specific aspects of the proposed project prior to submitting your application. If you have any general questions, please
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candidate should have a good 2.1 Bachelors, or Masters degree in Electronic, Chemical Engineering, Applied Physics, Physical Sciences or equivalent. Experience in one of the following topics will be a plus
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vitae (CV) Contact details for two proposed supervisors Names and contact details of two referees. Apply Finally, register and apply online. You'll receive an email acknowledgment once you've submitted