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Degree or equivalent Skills/Qualifications Essential Strong background in applied mathematics, mathematical biology, or computational modelling. Experience with partial differential equations (PDEs) and
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interdisciplinary research team. PhD in one of the following areas: infectious disease epidemiology, mathematics, statistics, physics, AI, computer science, population biology or a similarly quantitative discipline
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on studying the principles of neural computation through recurrent neural networks, dynamical systems theory, and machine learning. - Develop mathematical and computational models of neural networks - Analyze
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: machine learning, data analysis, statistical modelling, explainable AI, computational methods for large-scale data, and analysis of biomedical or population-based datasets. An interest in applications in
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Engineering, Medical Image Analysis, Applied Mathematics or a related field Experience with deep learning for image analysis, preferably in medical imaging Experience with generative modelling, ideally
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influence the adaptability and evolvability of diploids and polyploids. Your profile You have a PhD in Computational Biology, Evolutionary Biology, (Bio)Engineering, Mathematics or Physics. You have expertise
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mechanistic insight into the impact of WGDs on the evolution of molecular systems. In this project, we will use a mechanistic, sequence-based genotype-phenotype mapping model in combination with population
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population PK/PD, exposure-response, and mechanistic systems modeling approaches. They will collaborate closely with cross-functional partners within CQP and across the broader development organization
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support project leads at Moderna by evaluating and implementing model-informed drug development (MIDD) strategies, including population PK/PD, exposure–response, and mechanistic systems modeling approaches
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of neuroscience and new computational methods, combining mathematical and biophysical models with state-of-the-art diffusion and functional MRI data, in typically and atypically developing populations through