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challenges and real-world impact. Project overview In recent years, generative neural network models for creation of photo-realistic images have become increasingly popular. Their training results in a low
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conventional, frame-based sensors in comparison to event-based sensors, which are more commonly associated with neuromorphic computing. This work will support mission design decisions regarding the suitability
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different data collection methods and sensors used to gather road condition data, including TPMS (Tire Pressure Monitoring System), high-speed wheel encoders, CAN (Controller Area Network) data
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environmental and geological gradients. The successful postdoc will integrate the scientific community of the Nutribor project. Briefly, the Nutribor project consists of different workpackages. 1/in-situ
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carried out under the supervision of Dr Aisling O’ Driscoll and Professor Dirk Pesch. The appointee will work together with other Postdocs and PhD students on vehicular networking. The researcher will be
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-based sensor data to enhance the prediction of peatland soil properties and functions. You will focus on leveraging machine learning/deep learning techniques along with explainable artificial intelligence
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-based sensor data to enhance the prediction of peatland soil properties and functions. You will focus on leveraging machine learning/deep learning techniques along with explainable artificial intelligence
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and Development (EOARD) and is a full-time research position. Qualifications To be qualified for employment as postdoc, you must have been awarded a doctoral degree or have a foreign degree that is