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and fast learning ability are required. Preferred Qualifications: - Less than three years of post-graduate research experience in the fields of liver cancer, immunology, metabolic diseases, microRNA
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not reached vulnerable groups or created impact at scale. SMARTSCALE brings together scientists, local partners, and governments to learn from past experiences and design better ways to spread moisture
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advanced retrieval techniques, including spatio-temporal regularization, and hybrid methods with machine learning and deep learning. He/she will support the development of an improved forest RTM that can
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approaches beyond standard deep learning methods. Demonstrated experience in academic research (e. g. through publications, conference contributions or comparable scientific achievements). Very good
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the research activities entrusted to the officer take place: This ANR project lies at the interface between statistical learning (mainly deep learning) and combinatorial optimization (mainly stochastic and
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environment? If so, the Oak Ridge National Laboratory’s Learning Systems Group within the Data and Artificial Intelligence Systems section invites you to apply to our new postdoctoral research associate
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team at AMOLF, working on fundamental questions on physical self-learning systems as part of the NWO ENW‑M1 project “How do physical learning systems learn?”. The research position is intended to start
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Post-Doctoral Associate in the Center for Interdisciplinary Data Science and Artificial Intelligence
join forces to push the boundaries of data‑driven discovery. Our mission is two‑fold: to advance fundamental theory in probability and machine learning, and to translate those breakthroughs into high
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energy consumption in information processing and machine learning (e.g., arXiv:2308.15905); Quantum phenomena in information processing: exploring how quantum effects can be utilized to process information
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on the design of AI-based surrogates of large-scale energy system models and will be integrated in the activities of the Nexus-e group with close links to the ETH AI Center and the Reliability and Risk