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. This project will focus on developing the quantum control building blocks for implementing low-overhead quantum error correction on trapped ions-based quantum processors. Quantum control techniques can open
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have at least 2:1 degree in a relevant field. Applicants with the prior knowledge and experience in one or more of the following fields are encouraged to apply: computer programming (any programming
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chem- and bioinformatics to computer vision and social network analysis. Machine learning with graphs aims at exploiting the potential of the growing amount of structured data in all these areas
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that will consider the electromagnetic aspects, through computer modelling and simulation, and then identify material systems that enable the design and manufacture of antennas for test and characterisation
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is offering multiple studentships for candidates from backgrounds spanning the physical and computer sciences. These students will develop core expertise in robotic, digital, chemical and physical
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of Engineering), Mike Pound (Computer Vision, Computer Science Department), and Darren Wells (Plant and Crop Biophysics, School of Biosciences). Who we are looking for An enthusiastic, self-motivated, resourceful
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(particularly cognitive or applied psychology) Cognitive Science Human–Computer Interaction Engineering or Computer Science Health sciences Experience in empirical research, experimental design, data analysis
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in the course of the disease would result in better outcomes for the patient. We are looking for candidates to research computer vision and artificial intelligence techniques to create an objective
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programming and/or software engineering Electrical engineering and/or power systems. Application Procedure Informal enquiries are encouraged and should be addressed to Dr Jack Umenberger (jack.umenberger
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
intelligence, particularly in computer vision and deep learning, offer an opportunity to automate and enhance damage assessment by learning patterns from multimodal data. This research seeks to bridge the gap