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Field
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machine learning is essential; while structure prediction or materials chemistry experience would be advantageous, it is not a pre-requisite for the role. This post would be ideal for an ambitious and
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, data sciences, or a related subject, ideally with a focus on utilising large-scale health/biomedical data and the application of advanced data analysis methods such as machine-learning. Equivalent
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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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qualification/experience in a related field of study. The successful applicant will have expertise in statistical modelling, epidemiology or machine learning and possess sufficient specialist knowledge in
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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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, written, and oral communication skills in English. Exhibit strong organisational skills and the ability to meet deadlines and complete projects. Have expertise in machine learning and/or programming (highly
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approaches, machine learning) where appropriate. The successful candidate will actively promote FAIR data practices and will have opportunities to contribute to teaching, training, and wider community
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spatial transcriptomics and imaging genomics projects, integrating bulk and single-cell RNA-seq datasets, and applying advanced statistical and machine-learning methods (AI/ML) to extract novel biological
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looking for your next challenge? Do you have a background in machine learning or fluid dynamics and an interest in applying your skills to understand the dynamics of Earth’s fluid core and space-weather
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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