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Field
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system model new module integration, scenario simulations, and prognostics analyses Physics-informed deep learning/hybrid modeling/reasoning AI algorithm development and optimization Job Requirements: A
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developing/adapting computational models or algorithms to analyse biological data, and experience with single cell or spatial omics datasets or knowledge of cancer biology would be an advantage. What we offer
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, or similar languages Background in machine learning or deep learning methods Knowledge of genomics, transcriptomics, evolutionary biology, and plant biology is an advantage Familiarity with large biological
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and support all aspects of the research project. Carry out algorithm development and data analysis, including (but not limited to) single-cell and bulk transcriptomics, spatial omics, proteomics, and
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aligned with the aims of the Terascale project. Develop and apply suitable methodologies for research for AI for science and catalyst discovery. Design, implement, and evaluate novel models, algorithms
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technologies across a wide range of electrical machine topologies and applications. This includes research into permanent magnet and non-permanent magnet machine drives, high- performance control algorithms
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to advance precision medicine. Dr Chen’s research team develops AI algorithms for spatial and single-cell omics, integrates publicly available datasets into a centralised data hub, and leverages this platform
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framework to find the optimal operation strategy Conduct computer programming to verify the efficiency of the designed solution algorithms Analyze data acquired from the field survey Develop machine learning
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successful candidate will contribute to the EU projects (with funding support from Innovate UK and UKRI), which aim to develop wireless communication optimisation algorithms for digital healthcare applications
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. • Develop novel algorithms and methodologies for defect detection, classification, and failure analysis. • Supervise and mentor junior researchers and graduate students in related research activities. 2