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health data, such as electronic health records or biobank-scale resources (e.g., UK Biobank, All-of-Us, FinnGen). Familiarity with machine learning approaches, such as penalised regression, deep learning
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Engineering, etc.), expertise in cutting-edge AI and machine learning is essential; while structure prediction or materials chemistry experience would be advantageous, it is not a pre-requisite for the role
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. Preferred Qualifications: Prior experience working with mouse models of cancer is strongly preferred; candidates without prior experience will be considered if willing to learn. Interest in tumor metabolism
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thrombosis and lung injury in Sickle Cell Disease. The prospective candidate will have the opportunity to learn state-of-the-art techniques such as Multi-Photon-Excitation intravital microscopy of the lung and
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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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changes and established markers for Alzheimer's disease. The project may also include machine learning methods to estimate individuals' biological age. The project is based on existing data from a prominent
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range of teaching and life learning programmes which address the needs of students and professional groups who are interested in and undertaking work relevant to child health. GOS ICH holds an Athena SWAN
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(EHR), health information exchanges, and data analysis software. Experience with health IT innovation, including working with artificial intelligence, machine learning, telemedicine, or mobile health
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expertise in machine learning and/or Bayesian models is preferred. This position will involve both methodology development and analysis of multi-omic sequencing data, including spatial transcriptomic data
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, single-cell analysis, and machine/deep learning (preferred but not required). Strong programming and statistical skills (e.g., Python, Perl, R, Bash). Track record of first-author research papers. Strong