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to) fundamental research in machine learning or statistics, algorithm design, the application of AI methods in science, healthcare, social sciences, or business. You should have a PhD or equivalent level of
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are included but clinical medical themes are not covered, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data
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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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10 minutes and machine learning algorithms to deliver quantitative diagnosis without destroying the samples. The AF-Raman prototype will be integrated and tested in the operating theatre
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. You will also be responsible for implementing the model as a computer simulation and analysing it within a health-economics framework using standard computational techniques. You will also be
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, computational biology, computer science, data science or a related subject area and proven knowledge of python programming, developing machine learning/AI based tools and HPC. You will be expected to work as part
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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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quantitative and digital methods, such as descriptive/inferential statistics, data modelling, machine learning (ML), experimental prototyping and technology ideation. A significant degree of autonomy is required
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communication journals Demonstrable proficiency in advanced quantitative data analysis: applied machine learning, statistical analysis, and handling complex data. Programming skills in Python and R are essential
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themes are not covered, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML