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Primary supervisor - Prof Kate Kemsley Join us to research and develop advanced analytical methods for tackling food fraud head-on! Economically motivated adulteration of foods is a significant
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for greater precision. Machine learning (ML) algorithms will analyse these datasets to deliver a scalable, cost-effective system, validated through field trials and enhanced by contributions from four
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will work closely with the PI (Prof. Dawar) in a team of other researchers on the project and actively participate in the Logic and Algorithms research group. Candidates will have completed a Masters
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will work closely with the PI (Prof. Dawar) in a team of other researchers on the project and actively participate in the Logic and Algorithms research group. Candidates will have completed a Masters
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animals, while Prof Durbin's works on computational genomics and large scale genome science, including the development of new algorithms and statistical methods to study genome evolution. Moving forward
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environment-specific algorithms and machine learning approaches. At the end of the project a technology demonstrator will be built using UAV- and USV- mounted radar sensors, and it will be tested in real world
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approach could resolve this issue and we could use obtain data-driven models using machine learning algorithms such as artificial neural networks, reinforcement learning, and deep learning. A typical caveat
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fees and include a tax-free stipend (£19,237 pa. currently), for a period of 3.5 years. The successful candidate will be supervised by Prof. Kurt Debattista and Dr. Thomas Bashford-Rogers
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the form of a human-expert informed reward function. Second, we aim for the integration of low-energy machine learning algorithms, so that the resulting AI model can run on a variety of devices, including
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like the target voice, such as competing voices, which are particularly challenging for noise reduction algorithms currently employed in hearing aids. With multimodal hearing aids, which capture a