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to accelerate evaluation of costly simulations Genetic algorithms and other evolutionary techniques to generate a diverse set of high-performing solutions. You will design and implement new optimization
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slicing. - Develop advanced AI/ML algorithms and data analytics techniques to automate and optimise exposure requests, adapted to available resources and real-time demand. - Propose and
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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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techniques and the structure of bilevel problems in large-scale settings. Objectives The goal of this postdoctoral project is to develop scalable blackbox optimization algorithms tailored to bilevel problems
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suitable data models [CSC+23]. Objectives As far as the design of efficient numerical algorithms in an off-the-grid setting is concerned, the problem is challenging, since the optimization is defined in
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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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revisit discretization methodologies in view of modern requirements and computational capabilities. The candidate will focus on developing mesh generation algorithms meeting the following criteria
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algorithms for dynamic structured data, with a particular focus on time sequences of graphs, graph signals, and time sequences on groups and manifolds. Special emphasis will be placed on non-parametric
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(morphological patterns), based on the experts’ knowledge. Then, tools like Procrustes analysis, linear dimensionality reduction (PCA) and standard clustering algorithms are employed. A first objective of our
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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