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Field
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correlations or more innovative methods of multivariate analysis and we anticipate here an opportunity of using machine learning that could help in predicting properties or classifying sources. A last step will
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computational modeling and/or analysis of complex biological systems, integrating state of the art tools such as machine and deep learning approaches. Experience in managing biological databases and statistical
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experimental parameters (time, temperature). To optimize these parameters, active learning techniques based on Bayesian optimization will be applied. In situ or ex situ characterizations (FTIR, ¹¹B/¹H NMR, HP
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to candidates from a broad range of AI subfields, including, but not limited to machine learning, generative AI, computer vision, representation and reasoning, natural language processing
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Context and Motivation Bilevel optimization problems, in which one optimization problem is nested within another, arise in a wide range of machine learning settings. Typical examples include
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parameter estimation using Bayesian inference, and/or the exploitation of Machine Learning (ML) based algorithms to reduce false positives caused by human generated interference signals in the observational
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technologies, and integrate machine learning-driven digital twins for predictive combustion modeling. The research program will cover a wide range of e-fuels (H₂, NH₃, CH₃OH, DME, OME) and their applications in
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(formulation, algorithms, applications in structural mechanics), HPC computing, reduced-order modelling, machine learning, Vibrations and structural dynamics, architected materials, Additive manufacturing
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-flexible technologies, and integrate machine learning-driven digital twins for predictive combustion modeling. The research program will cover a wide range of e-fuels (H₂, NH₃, CH₃OH, DME, OME) and their
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new insights into the laser-matter interaction mechanisms for laser material processing applications. References [1] High aspect ratio nanochannel machining using single shot femtosecond Bessel beams M