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Field
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engineering, machine learning, molecular design, and sustainability, helping to create smarter ways of identifying promising sorbents for electrochemical CO2 capture. Over the course of the project
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‑mining and machine‑learning methods. The expected scientific outcome is to establish guidelines for identifying and optimizing promising electrolyte materials and to support the development of future
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research in one of the Department's key research areas: (i) Artificial Intelligence and Machine Learning; (ii) Big Data Analytics and Management; (iii) Computer Vision and Pattern Recognition; and (iv
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research in one of the Department's key research areas: (i) Artificial Intelligence and Machine Learning; (ii) Big Data and Data Management; (iii) Computer Vision and Pattern Recognition; and (iv
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English. Preference will be given to those with (i) strong background in quantitative methods, geospatial methods, AI and machine learning; (ii) experience in high-performance and cloud computing; (iii
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(DInSAR). Minute surface uplift and subsidence signals will be automatically detected using machine-learning workflows, enabling systematic, user-independent identification of drainage events every 6–12
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diffraction data where the information extends towards 3-d space. Machine learning offers promising approaches for the solution of complex problems of disorder, ultimately aiming at general and automated
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- When and where do we reach the limits of adaptation to riverine flood risk?”. You have experience in machine learning, programming and flood risk research. If so, we encourage you to apply! You will
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Research Framework Programme? Horizon Europe - ERC Is the Job related to staff position within a Research Infrastructure? No Offer Description The Machine Learning for Integrative Genomics team (https
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-FNR PEARL Research Grant in the area of Information Systems Engineering, and, depending on interest, in fields, such as Generative AI & Machine Learning, Data Privacy, Cyber Security, Digital Identities