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Field
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, finance, and healthcare, where data integrity and system reliability are non-negotiable. This PhD project addresses the integration of robust security measures within AI-enabled electronic systems
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, machine learning, and information-theoretic approaches to achieve robust, non-intrusive security for the ever-expanding IoT landscape. Feature Engineering for Encrypted Traffic: It is crucial to identify
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Chemistry, Physics, Materials Science, or a related discipline. The successful applicant will demonstrate strong interest and self-motivation in the subject and the ability to think analytically and
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the recording of substrate-borne vibrations produced by earthworms (and other invertebrates) in situ. To fully exploit this emerging technology there is a need to unravel the meaning within the data. As such
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Application deadline: All year round Research theme: Computational Chemistry, Material Science No. of positions: 1 Eligibility: UK students This 3.5-year project is fully funded by industry and home
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Discipline: Engineering & Technology, Civil Engineering Qualification: Doctor of Philosophy in Engineering (PhD) Eligibility: All qualified candidates (both UK and International students) Award
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orbital parameter extraction using image processing techniques. The ideal candidate should have a strong background in physics, engineering or a related field, as well as experience working with programming
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useful information for a further clinical trial using combined therapy. Ultimately, the translational potential of these findings could markedly improve public health outcomes and quality of life
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biology, and biochemistry. Plan, conduct, and contribute to experimental work. Identify and develop relevant methods for data collection, analysis, and interpretation. Develop expertise across scientific
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such approaches would be cost effective. To achieve this, you will be supported to undertake analyses using large data registries such as the Clinical Practice Research Datalink. This is an exceptional opportunity