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Research Fellow in AI for Healthcare (1 FTE) – ArcHub ICUSafeNotes-School of Computer Science 011287
breakthroughs into commercial healthcare solutions. In the ICUSafeNotes project, we are conducting research and development of multimodal machine learning models that includes ICU note summarisation and analysis
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will investigate phage-mediated horizontal gene transfer, employing approaches such as long-read high-throughput sequencing, faecal chemostat models, random transposon insertion libraries in gut bacteria
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functional characterisation (XRD, SEM, Raman, FTIR, gas sorption analysis) to identify materials with high hydrogen capacity (>7 wt%), improved stability, and long cycle life. Computational modelling and
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emphasis on machine learning applications in asset pricing and corporate finance, alongside traditional econometric and factor modelling techniques. The successful candidate will play a pivotal role in
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development, budget control, and reporting to senior governance bodies. 2. Years of experience delivering complex construction or transformation projects in regulated, multi-stakeholder environments, including
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). This work is part of a larger project aiming to develop novel battery thermal management systems (BTMS). The main tasks include (i) The development of an integrated model for the BMS-BTMS
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team and participate in our vibrant research community here at CSIT. Project Title: Fault-tolerant Wireless Time Sensitive Networks This project builds on our ongoing work on modelling and simulating
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leadership on the use of generative AI for developing the machine learning model, system integration and deployment, analysis and optimisation, working closely with the Principal Investigator and managing
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Generators, and hardware-accelerated primitives, the project establishes a dual-layer security model that fortifies data protection across diverse infrastructures. Leveraging hardware-accelerated primitives
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the CFD component of the DETECT project and support other aspects of the project (management, reporting and fieldwork). The overall project aims to assess coastal vulnerability and model it using a