20 condition-monitoring-machine-learning Fellowship positions at Nature Careers in Uk
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International travel may be required for this role. Background This post will advance the application of Machine Learning (ML) in weather forecasting and hydrological prediction. The Research Fellow will develop
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foundations of quantum adversarial machine learning, an emerging field at the intersection of quantum computing and machine learning. It investigates how the unique capabilities of quantum computing can be
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Jan. 2026, based in the University of Birmingham UK. This position will use further develop the novel AI/machine-learning (ML) approach in Chen et al. (2022 & 2024, Nature Geoscience ) and apply
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annotation of these metabolomes using multistage fragmentation (MSⁿ) data, incorporating novel computational methods and strategies (e.g. spectral matching, network-based approaches, machine learning) where
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using hybrid models combining mechanistic, GenAI, and machine learning approaches. You’ll contribute to building disease-specific Digital Twins using large-scale single-cell multi-omics datasets
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, 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 methodology project
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resting conditions. The researcher will use a combination of synovial tissue organoid systems and transgenic mouse models to delineate the role of the proteoglycan-4 (the gene that encodes lubricin) in
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developing and implementing machine learning/AI solutions using relevant languages and frameworks Excellent communication skills and proven ability to collaborate with diverse stakeholders Technology and
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/software monitoring etc Demonstrate an understanding of practical applications of bioinformatics for immunological or inflammation research Ability to assess resource requirements and use resources
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understand the conditions required to ensure the sustainable management of lithium-ion batteries when they reach the end of their useful life in electric vehicles. This will enhance the overall efficiency