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Group , a leader in innovative multi-sensor atmospheric remote sensing from ground, airborne, and satellite platforms. Our group develops advanced algorithms and data analysis methods to address
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our daily lives. Technology such as the electricity grid, which our faculty is helping to make completely sustainable and future-proof. At the same time, we are developing the chips and sensors
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completely sustainable and future-proof. At the same time, we are developing the chips and sensors of the future, whilst also setting the foundations for the software technologies to run on this new generation
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prior to the application deadline Research experience with deep learning architectures (e.g. Transformers, diffusion models, graph neural networks) applied to multimodal data. Proven expertise in time
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the University of Beira Interior, Covilhã, Portugal, under the following conditions: Scientific Area: Electrical and Computer Engineering. Objectives: To develop an interoperable transducer network to support the
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wastewater. This includes sensors and sensor networks for detection of these targets and testbeds for the validation of technologies developed. INSTITUTIONS HIRING: Northwestern University University
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such as the electricity grid, which our faculty is helping to make completely sustainable and future-proof. At the same time, we are developing the chips and sensors of the future, whilst also setting
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sensors in composites. Strong mathematical background and experience with numerical methods for structural analysis. Proficiency in programming (e.g., MATLAB, Python). You have obtained your PhD degree in
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biologging multi-sensor acoustic tags on bats and toothed whales in the wild and combine these data with array recordings, noise playbacks and phantom target generators on trained animals in the lab
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systems (ITS). In particular, the successful candidate will conduct cutting-edge research in: Developing physics-informed neural networks (PINNs) for complex dynamical systems modeling and observer design