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the world and there is an urgent need to have better prognosis and predictive biomarkers, in order to improve the optimal care of these patients. Many existing therapies lead to an improvement of the overal
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setup, process optimization, and safe, efficient upscaling strategies across various research projects. This position is ideal for someone with a solid understanding of chemistry and polymer science
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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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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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of patients treated with immune-checkpoints inhibitors. Our final clinical goals are to help to generate new data-driven tumor response criteria, specifically adapted to immunotherapy, so as to optimize
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research programme and infrastructure for AI-driven analysis of biomedical data, focusing on precision medicine applications in disease prediction, diagnosis and treatment optimization Promoting and driving