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, Applied Mathematics in Biology, or Bioinformatics. • Solid understanding of the basic concepts of evolutionary theory and population genetics. • Proven programming skills (SLiM or C++ highly desired
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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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follow the Graduate Program "Mathematics and Applications: Research and Interactions" (https://irmiapp.unistra.fr/training/presentation ). PHD Country: France Where to apply Website https://www.abg.asso.fr
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aspects of the PhD, namely on the one hand conducting experiments and behavioural analyses, and on the other hand developing and testing complex mathematical models that capture the dynamics of the networks
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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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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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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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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