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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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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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characterization and large-scale component testing, including the setup and use of conventional and advanced sensing technologies (e.g., displacement/strain sensors, DIC, fiber-optic sensing). Analyze experimental
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is currently building and commissioning a network of calibrated multi-sensor observatory-class systems, and developing novel machine learning methods with the aim of collecting science-quality data
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maintain and develop the gravity and height networks of Denmark and Greenland on behalf of their respective governments. Responsibilities and qualifications As a post doc working with airborne gravimetry
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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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. To reach level-4/5 autonomy, we need teamwork: nearby vehicles, drones, and roadside units must co-perceive their environment, sharing and fusing complementary sensor views in real time. Yet raw video
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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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data from measurement and observation networks, as well as snow testing protocols, to provide access to a larger, harmonized information system on both sides of the border; Mutual training and