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component disciplines; in explainable multi-modal deep learning models, in causal statistical models and in human-machine teaming and AI ethics. The researcher will conduct internationally-leading research in
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deep learning models, in causal statistical models and in human-machine teaming and AI ethics. The researcher will conduct internationally-leading research in human factors with applications
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-related subject such as health technology assessment, medical statistics, epidemiology, psychology, public health, health promotion/education Excellent writing and communication skills Knowledge of, and/or
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statistical techniques. Applicants should be fluent in Python, experienced with modern machine learning frameworks such as PyTorch or TensorFlow, and comfortable working with large, complex climate datasets in
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partners and stakeholders across countries, statistical offices, international agencies and Ministerial teams. This role would suit someone with a highly professional approach and a strong track record
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database and statistical analysis of the data is clear, quality controlled and accessible to other members of the research team. This full-time position is offered for a fixed-term until 31/12/2026. The post
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, generative models, or equation discovery — rather than traditional statistical techniques. Applicants should be fluent in Python, experienced with modern machine learning frameworks such as PyTorch
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resolution national, regional and global demographic estimates. They will explore, test and validate novel statistical and AI methods, working with partners from governments and UN agencies to big tech and
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design, statistical analysis, and results reporting. The role will also involve planning and managing project activities, building research skills and networks, and engaging with academic and industrial
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reflection and learning. The postholder will engage with a range of WorldPop’s external partners and stakeholders across countries, statistical offices, international agencies and Ministerial teams. This role