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statistical and machine learning, deep learning, chemometrics, multimodal data fusion, computer vision, uncertainty-aware modeling, stochastic control, optimization, and deployable edge-to-cloud decision
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. In the ELUD research project, we address the question of if and when learning agents converge to an efficient equilibrium and when this is not the case. ELUD will design new algorithms for computing
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Apply now Job no:535376 Work type:Faculty - Pro Tempore Location:Eugene, OR Categories:Instruction, Computer and Information Science, Data Science Department: CAS CIS Computer & Information
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Science Initiative has supported the rapid growth of the departments of Statistics & Data Science and Computer Science, as well as many interdisciplinary activities in which the postdocs could participate
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University We are looking for a highly motivated postdoctoral researcher with a strong interest in optimization. The position will involve the development of novel reformulation and algorithmic methods
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through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Within the **prairie-psai** cluster
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Post-Doctoral Associate in the Center for Interdisciplinary Data Science and Artificial Intelligence
the theoretical foundations of data science and AI. Responsibilities: Conduct research on automated reasoning and proof checker, AI-assisted collaboration. Develop and analyze algorithms for learning and
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-series data, or other high-dimensional biomedical datasets. Strong foundation in signal processing, statistical analysis, computational modeling, or algorithm development. Ability to process, clean
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Associate Research Scientist / Post-Doctoral Associate in the Division of Science (Computer Science)
machine learning. The successful applicant will participate in research involving human computation, knowledge discovery, machine learning, and data science. The position will provide the opportunity
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quantum matter and quantum computation. Our research includes (1) AI for Quantum: developing advanced AI algorithms grounded in physical principles and machine learning theory to tackle key challenges in