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Field
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-readable representations, such as distributed representations of text augmented with random noises [1] or unnatural text curated by replacing sensitive tokens with random non-sensitive ones [2]. First, such
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, but also in traffic monitoring or in the media context, for example when it comes to automatic metadata extraction and audio manipulation detection. Another focus is the development of algorithms
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distributions. We wish to represent the biological networks into proper formats, e.g., vector representations, so that existing machine learning algorithms (e.g., support vector machines) can readily be used
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Qualifications: Education: BS in Geospatial Data Science, Geographic Information Science, Computer Science or close equivalent. Experience: Experience developing and optimizing algorithms. Extensive programming
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investigation and developing software algorithms and techniques to support human or machine information interactions for the purpose of information retrieval/dissemination, analysis and/or decision making
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algorithms) that enable rapid creation of new high-fidelity multi-scale/multi-physics computer models of materials capable of utilizing modern extreme-scale computing environments. The success of multi-scale
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Mexico, and elsewhere), and in relating particle composition to measurements of size distributions, air mass trajectories, etc.; (2) development of algorithms to process complex spectral data and identify
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the development of image/signal processing algorithms from a multidisciplinary approach, to include multiple sensor modalities. These multidisciplinary research opportunities incorporate theoretical and
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and often different from the canonical types of data used to benchmark machine learning (ML) algorithms. In this opportunity, we will be evaluating how state-of-the-art ML techniques can be used