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Field
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will contribute to the field by: Developing a conversational AI interviewer capable of conducting real-time adaptive interviews. Building an automated candidate ranking model based on interview
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to modulate pathological cell behaviour in vitro, with potential for progression to in vivo testing in a rodent model of glaucoma. Candidate Profile Applicants should hold (or expect to obtain) a 1st or 2:1
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process conditions. Furthermore, this research will focus on the development of a model, allowing for virtual testing and optimisation of the chemical recycling process. This includes potential
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-fueled engine, power converters, and a battery energy buffer. Key tasks will include developing system-level models, optimising energy management, and coordinating power flow between subsystems to maximise
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for household who stay indoors, and to prepare for emergency responses. Possible quantitative methodologies include concurrent time-series analysis of outdoor and indoor environment data, prediction model
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dynamics and tissue morphogenesis during embryo development using cellular, molecular and mechanical approaches. Cell movements underlie tissue patterns and shapes. Using chick embryos as the model system
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prostate cancer risk across diverse ethnic groups. This work aims to support more equitable risk stratification in cancer screening programmes. Using simulations based on multistate modelling framework
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modelling to provide a robust framework for integrating nature-based solutions into SO management. This can alleviate the pressure on treatment infrastructure and reduce dependence on grey infrastructure
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breast cancer in animal models (4). In this PhD project, we will investigate: The effect of combined treatment with ITCs and a selected anti-cancer drug (sorafenib or triptolide) on breast cancer will be
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expertise and facilities in electrochemistry, materials chemistry, advanced characterisation techniques (including a variety of spectroscopy, microscopy,) modelling and battery and fuel cell construction and