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nutrition, such as: analysis of time series data and dynamic processes, where signals and responses evolve over time. statistical modelling, AI, and machine learning on large epidemiological cohorts, diet and
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intersection of machine learning and life sciences, developing next-generation models that improve our understanding of human biology and enable more proactive, personalized healthcare. As an Industrial PhD
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in English (reading, writing, speaking). • Show ability to work independently as well as in a team. • Good knowledge in AI, machine learning, data science and mathematics. • Good knowledge in one
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or heterogeneous environmental datasets Familiarity with spatial analysis, GIS, or geospatial data workflows. Experience with machine learning, modelling, or systems analysis approaches Interest in resilience
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, engineering physics, physics, machine learning, or in a similar subject, or have completed at least 240 credits in higher education, with at least 60 credits at Master’s level including an independent project
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multi-omics integration with advanced machine learning, including artificial neural networks, to predict disease-relevant splice variants across cardiometabolic diseases. By leveraging extensive meta
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research experience in e-health, digital health or a related field experience of, or a documented interest in, machine learning, AI methods or large language models (LLMs) in clinical or health-related