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dietary exposure risks to consumers. The research will involve method development using LC-MS/MS for toxin detection, sample collection, data analysis, and comparison of results against current safety
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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 of data-driven modelling
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, develop business cases and more. At the 6-month point, students progress onto their interdisciplinary PhD research project, supervised jointly by two academics from two research groups. Usually, supervisors
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meetings. Project Background This project directly supports QCI3's vision of integrated and interconnected implementations by developing essential benchmarking tools that bridge across all three themes
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an important role in the efficient integration and management of solar energy in modern power systems. The studentship project aims to develop a novel PV forecasting model based on physics-informed neural
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both sites. The project sits at the interface of cell line engineering, protein science and machine learning and you will receive advanced training in these areas while developing methods to accelerate
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own idea or one developed with your supervisors, but it must be relevant to local health needs in the Global South. What’s included in the fellowship? We offer a comprehensive package to support your
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to cancer biology, as well as a strong commitment of developing and using new tools to address cutting-edge questions in these fields. This studentship is embedded within the piRNA team, consisting of both
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, introduces human error, and creates line-of-sight occlusions, disrupting surgical workflow. This interdisciplinary project aims to overcome these challenges by developing a vision-based marker-less navigation
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mechanical fatigue—individually and sequentially, followed by electrical breakdown testing to assess their impact on the dielectric performance of the material subsystems. The target is to develop a