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focuses on leveraging zebrafish as a model organism to develop and optimize genetic tools through a directed evolution pipeline, with significant therapeutic and industrial applications. Key
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relevant not only for the organizations developing, training, or optimizing AI models, but in particular for users of the software products that inform and impact the policies that will regulate the AI
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teaching in key and rapidly evolving areas such as autonomous systems, data-driven modeling, learning-based control, optimization, complex networks, and sensor fusion. Research at the division is
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distributed computational pipelines and optimizing communication costs. You will also contribute to the integration and testing of the models in real D-MIMO environments, in close collaboration with a PhD
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algorithms. Our research integrates expertise from machine learning, optimization, control theory, and applied mathematics, spanning diverse application domains such as medicine, energy systems, biomedical
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existing and creating new deep learning-based models for anomaly detection, theoretical and numerical studies of detection quality, creating new distributed computational pipelines and optimizing
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, spectroscopic signatures, microstructural images, processing conditions, and macroscale performance will be used for the optimization of materials. The candidate will collaborate extensively with in
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to efficiently transform biochar into battery-grade hard carbons with controlled characteristics and optimized electrochemical performance. The use of microwave plasma carbonisation will allow
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such as satellites, LEO constellations, fixed links, automotive radar, and Earth observation stations, using techniques from signal processing, communications theory, optimization, and AI. You will join the
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innovative approaches to design and optimize materials with enhanced bioactivity. Experience in design and synthesis of peptides will be considered a strong merit, as well as expertise in chemical modification