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Field
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-sensor fusion, and propagation modeling — to develop AI-enabled detection, classification, and triangulation algorithms for critical energy infrastructure applications. This position resides within
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Group (EASE IRTG), Empowering Digital Media (EDM), the Research Training Group HEARAZ , the Research Training Group KD²School (KD²School), π³: Parameter Identification – Analysis, Algorithms
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, the objective of this thesis is to develop and test a real-time, tightly-coupled traversability algorithm that fuses information from both sensor modalities, therefore providing a more complete understanding of
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to research, develop, and evaluate algorithms for a variety of geospatial, signal, and/or image processing applications. The successful candidate will have a solid background in computer science with strong
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., LEEDR). Additional efforts involve innovative techniques like convolutional spectroscopy, drone-mounted sensors, and experiments to understand the impact of aerosols on thermal blooming, cloud
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next-generation multiphysics tomography sensors for real-time monitoring of industrial flows. By embedding AI directly into sensing hardware, fluiidd enables industries to “see” inside opaque systems and
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analysis; (b) develop and implement algorithms for 3D perception (e.g. segmentation, localization and mapping); (c) design and execute experiments to evaluate, validate and refine algorithms and
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the wild? You are excited about deploying autonomy algorithms on real platforms and validating them in demanding real-world environments? We are looking for an outstanding and highly motivated doctoral
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working with senior technical staff to research, design, develop, integrate, and test advanced optical sensor algorithms in constrained processing architectures. This position will be fully on-site in State
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to transform EO data into reliable, transparent and actionable intelligence is critical. You will explore novel approaches for exploiting multi-sensor satellite data — such as optical, SAR, thermal, and