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off-the-shelf sensors and the development of resilient algorithms that combine first-principles modeling with modern machine learning techniques. The goal is to push the boundaries of robust perception
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. Kinetic rates will be calculated on the fly from molecular dynamics simulations using machine learning potentials. This approach will provide guidelines to steer the formation process of zeolites by tuning
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large research group which is well connected to the machine and transportation industry and I am eager to learn how academic research can be linked to industrial innovation roadmaps. - During my PhD I
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to use qualitative and quantitative tools to measure technological competition, as well as markets and patent databases, which will then be analysed using network analysis and machine learning. Empirically
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models. By utilizing biobanks of lung biopsies and established animal models, LifeLUNG will apply advanced machine learning and AI-driven deep sequencing to identify key immune factors and gene targets
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-Molecule Drug Discovery Strong foundation in computational methods (e.g. docking, molecular dynamics, QSAR, machine learning) Experience working across diverse target classes Demonstrated impact on
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laude level. You are interested in both Machine Learning and Symbolic/Logic-based AI methods. You strive for excellence and have a scientific mindset. You are a loyal team player, who can work
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to model zeolite formation as a dynamic network of growing and dissolving clusters. Kinetic rates will be calculated on the fly from molecular dynamics simulations using machine learning potentials
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knowledge of wireless communications, and signal processing. You have at least intermediary knowledge of machine learning algorithms, including federated learning, split learning, and graph neural networks
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-of-the-art in SLAM, situational awareness, computer vision, machine learning, robotics, and related fields Developing and implementing innovative solutions, validated through real datasets and experiments