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Field
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validate a comprehensive genetic panel for the molecular diagnosis of pituitary diseases. Using next-generation sequencing (NGS) technologies and a bioinformatics algorithm, the panel will identify genetic
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multiple radar units into autonomous vehicle platforms and evaluate perception algorithms in diverse scenarios. The successful candidate will work closely with our industrial partners and will analyse
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and machine learning algorithms to analyse and interpret the acquired data. The successful candidate will work primarily with Dr Qimei Zhang in the Department of Engineering at the Nottingham Trent
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validate a comprehensive genetic panel for the molecular diagnosis of pituitary diseases. Using next-generation sequencing (NGS) technologies and a bioinformatics algorithm, the panel will identify genetic
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of optimization and generalization to inform the design of practical training algorithms. To produce high-quality publications in top-tier machine learning conferences and journals. To offer guidance and assistance
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ideas from machine learning, econometric choice modelling and mathematical psychology. You will investigate how large language models, optimisation algorithms and automated model search methods can
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mathematical or computational algorithms. Strong scientific programming skills. Evidence of the ability to conduct research independently and to identify and address challenging research questions. Evidence of
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validate wearable data algorithms, software tools and pipelines supporting large-scale, multi-cohort research. It will contribute to the ProPASS federated data analysis platform, including testing
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sustainable manner. Key Responsibilities: Responsible for developing explainable machine learning algorithms for Tunnel Boring Machine (TBM) tunnelling and excavation Developing large language model enhanced
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for partial differential equations, and multiscale partial differential equations. The candidate will develop efficient numerical methods for solving the problems, and implement the numerical algorithms