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Field
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Methods Soft lithography and microfabrication to create defined surface landscapes. • Mating assays between resistant and sensitive Enterobacteriaceae strains. • Live-cell imaging and fluorescent plasmid
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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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Programme (NSIP). The position is full-time and fixed-term until 31 March 2027. This interdisciplinary project is conducted in collaboration with the Department of Plant Sciences and other industry partners
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results from human decision-making to inform the design of this new paradigm, and feed the results of the latter back into human decision-making to help make it more explainable. The PhD student will: (1
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to measurable safety metrics and relevant regulatory norms. Automate this pipeline to support scalable, simulation-based compliance checking. The outcomes will include a formal DCD framework, a prototype
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will be imaged across different field and thermal histories using MFM, magneto-optical Kerr effect (MOKE) imaging, and synchrotron-based X-ray magnetic circular dichroism (XMCD). In parallel, electrical
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function and develop science-based advice in fisheries management to prevent significant adverse impacts. This project will combine seafloor visual surveys, targeted sampling and AI-assisted image analyses
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combination of experimental work, rig development, and computational modelling, and will equip the student with a unique skill set highly relevant to both academic research and industrial careers such as
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(R1) Positions PhD Positions Country United Kingdom Application Deadline 3 Dec 2025 - 23:59 (Europe/London) Type of Contract Temporary Job Status Full-time Hours Per Week 35 Offer Starting Date 1 Jan
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discovery, and deep-learning algorithms • Neutron scattering and advanced characterisation techniques The successful candidate will work closely with other PhDs and postdocs involved in similar investigations