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, transport, or defense. On the technical side, we aim at combining statistical latent variable models with deep learning algorithms to justify existing results and allow a better understanding of their
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the technical side, we aim at combining statistical latent variable models with deep learning algorithms to justify existing results and allow a better understanding of their performances and their limitations
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research work will be to devise efficient algorithms for source separation in DAS measurements. Issues such as large data volumes that can exceed 1 To per day and per fiber, instrument noise, complex nature
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molecular targets critical for developing new therapies for rare diseases, based on genetic data and biological system simulations. -Computational Drug Repurposing: Developing novel algorithms and databases
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analyzed. The tensor model structure estimated by suitable optimization algorithms, such as that recently developed in [GOU20], will be considered as a starting point. • Exploiting data multimodality and
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equipped biophysical analysis platform. 4) Nanobody production. 5) X-ray crystallography facility and 6) Cryo-electron microscopy facility equipped with a Glacios Thermofisher TEM. Requirements A PhD degree
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languages and in R is also required. • Application deadline: 1st August 2025 • Required experience: 2 - 8 years after PhD. • Duration of the contract: 36 months • Salary: Upon experience and according
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the use of synthetic data in precision medicine research and applications through development of AI algorithms, tools and other processes to allow for the enrichment of clinical data sets Providing training
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to modelise DNA repairs Test of various algorithms to solve these models Development of an highly efficient code, based on Kokkos, to implement the previous Skills : You have a master and/or an engineering
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: 305,000 euros total funding over the project duration, including: • 199,000 euros for contractual collaborators (PhD students, postdocs, research assistants) • 106,000 euros for operational expenses