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requirements and focusing on data-value maximisation. This project will utilise innovative machine learning methods and tools from process systems engineering to simultaneously optimise product quality and the
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collaborations and perform cross-species comparisons. We use machine learning techniques for neural data analysis and computational modelling with a special interest in biologically-inspired deep learning and AI
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physics-integrated machine learning models—to predict, analyze, engineer, and understand microbial community dynamics. Applications span precision medicine and built environment microbiomes, with a strong
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of Environmental Engineering, ETH Zürich and matriculate in ETH Zürich. The research is related to development of experimental and modeling techniques to identify emission sources, simulate the airborne transport
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single‑cell omics, AI machine learning, and translational biology. The role involves collaboration with academic research group(s), with a strong focus on bridging advanced computational methods
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in modelling, simulation, or data analysis of energy systems Knowledge of machine learning or artificial intelligence methods Programming experience (e.g., Python, MATLAB or similar tools) Experience
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participant outcomes. The project will use a variety of approaches, including human perceptual experiments, machine learning, digital signal processing, and computational models of hearing. UConn has a vibrant
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-physics modelling of power electronic systems and components, with special focus of magnetic components, Incorporating physics-driven machine learning approaches in power electronics design, Incorporating
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collaborators The Machine Learning for Integrative Genomics team (https://research.pasteur.fr/en/team/machine-learning-for-integrative - genomics/) at Institut Pasteur, led by Laura Cantini, works at
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, implementation, and analysis of machine learning models for computer vision tasks (40%). Analysis of natural scene statistics in aquatic and terrestrial environments (40%). Design of models to learn texture