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in statistics, machine learning, mathematical modelling, or a related field, to join our research team in the Department of Applied Health Sciences. The successful candidate will work on an NIHR funded
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of fungal pathogens. The successful candidate will make extensive use of a range of in vivo models of fungal infection and will have access to the wide range of transgenic tools and in vivo immunology
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execution) Key Responsibilities Extend existing quantum path integral algorithms to diffusion model settings; Formulate and prove complexity theorems for the new algorithms; Develop and test prototype quantum
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. Hands-on expertise with microcontrollers (e.g. Arduino, STM32), embedded C/C++, and firmware development. Familiarity with BMS development or experience with other complex control systems such as motor
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of fungal pathogens. The successful candidate will make extensive use of a range of in vivo models of fungal infection and will have access to the wide range of transgenic tools and in vivo immunology
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leveraged to accelerate learning from both classical and quantum data. The project will develop rigorous theoretical frameworks to understand key properties of quantum machine learning models—expressivity
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models to predict TCR-peptide/MHC (pMHC) interactions. Utilizing structural computational biology techniques to characterize and model TCR-pMHC interactions. Designing experiments to test and validate
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. Working closely with the ABI program’s leadership team, you’ll contribute to data curation, protocol development, predictive modelling, and novel analytics for large-scale neurocritical care datasets. As
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to data collection Operate within area of specialism Analyse and interpret research findings and results Contribute to generating funding Ability to communicate complex information clearly Main Duties
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Ability to communicate complex information clearly Fluency in relevant models, techniques or methods and ability to contribute to developing new ones Ability to assess resource requirements and use