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on stochastic Riemannian optimization algorithms, these methods still suffer from limitations in computational complexity. The post-doctoral fellow will build upon this preliminary work to investigate
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for computational efficiency, ensuring scalability to large-scale datasets, and their performance will be analyzed. The project will explore applications in smart city monitoring, an area where the team has
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responsible to develop and test the new software in collaboration with experts of genomics at Joliot and of computer science at MdS. Your mission will include: Discussions and set-up of the physical models
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within a coherent computational model is currently challenging, due to the typical large dimension and complexity of biomedical data, and the relative low sample size available in typical clinical studies
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(FSTM) at the University of Luxembourg contributes multidisciplinary expertise in the fields of Mathematics, Physics, Engineering, Computer Science, Life Sciences and Medicine. Through its dual mission
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impact A demonstrated commitment to mentoring and fostering the next generation of researchers Willingness to contribute to teaching programmes at the undergraduate and graduate levels in areas related
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generation of researchers Willingness to contribute to teaching programmes at the undergraduate and graduate levels in areas related to global health, environmental change, and socio-environmental systems
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ability to work in a cooperative, multi-cultural and multi-disciplinary environment. Dynamism, self-organization, autonomy and drive. Interest for computing biology (R programming, image analysis) will be
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. Interest for computing biology (R programming, image analysis) will be an additional asset. Contact & applications: Applications should be sent to pierre.guermonprez@pasteur.fr ; julie.helft@inserm.fr
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, computer science, population biology or a similarly quantitative discipline.