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machine learning methods to improve the understanding, treatment and prevention of human disease. The successful candidate will develop novel statistical and machine learning algorithms to address key
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it; as well as have theoretical skills including algorithm implementation/development and data visualization. Experience and interests include designing machine learning pipelines, building web
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control, machine learning, and differential geometry to work on the development of advanced algorithms to enhance the safety and robustness of human-robot interaction. The successful applicant will engage
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magnetic resonance imaging using pulse sequence design, image reconstruction, and real-time image processing, for brain, body, and fetal applications. Responsibilities: The successful candidates will develop
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process modelling, experimental data, model parameters and modelling approaches in order to optimize design, analysis and operation of complete capture processes. The goal of the project is to develop
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/Machine Learning (AI-ML) approaches to meeting this challenge. Possible topics include, but are not limited to: storylines for plausible narratives of regional climate change, novel algorithms for rare
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(including a Parenting and Carers Fund and the Carer’s Career Development Fun d), training, and a variety of diversity and inclusion networks. Staff can apply for flexible working to help them balance
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will be tailored to your expertise, spanning from hardware design to system-level optimization and control methods. For the AI position, you will develop machine learning models that incorporate physical
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for plausible narratives of regional climate change, novel algorithms for rare event sampling or ensemble boosting, and the development and use of hybrid climate models combining physics-based and ML components
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, working on the development of advanced AI/ML algorithms for battery management systems (BMS) in electric mobility and micro mobility applications. The primary focus will be on creating and optimizing state