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an advantage: plasma surface functionalization; electrode/electrolyte interfaces; battery degradation modelling; microstructure-resolved modelling; tomography or image-based electrode modelling; machine learning
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and predict how the immune system responds to interventions. This tight integration of advanced machine learning and experimental immunology allows us to tackle fundamental biological questions with
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-level modelling of environmental exposures and health risks; Interpretable and uncertainty-aware machine learning for heterogeneous health data. This position offers the opportunity to work in a
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and adapt assimilation schemes based on generative deep learning methods (such as flow matching and diffusion models). The candidate should have previous experience in data assimilation and/or deep
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-coordinate the ARCHAI project, ensuring close collaboration between the AMGC and FLAIR teams Contribute to the development of machine learning methods for archaeological predictive modelling / site discovery
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of machine learning methods for archaeological predictive modelling / site discovery Contribute to the design of the archaeological search-process simulator used to train and evaluate AI methods Write grant
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of thousands of molecules in our fully automated high biosafety screening facility CAPS-IT against multiple viruses. You will be responsible for the development and deployment of advanced machine learning models
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critical mind, and motivation are skills that are more than welcome Knowledge of both the basics and the latest developments of machine learning, in particular large language models and agentic framewoks
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-scale nature, complexity, and heterogeneity of 6G networks, we use tools such as artificial intelligence/machine learning, quantum computing, graph theory, graph-signal processing, and convex/non-convex
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different types of epidemic models, including spatial and individual-based models. Methodologically, the work will focus on multi-objective, hierarchical, and explainable reinforcement learning, as