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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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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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application, e.g. enhancing the stability of a clock by breaking it up into sub-ensembles, or applying different sequences to distinct clocks in a network in order to search for transient frequency shifts as a
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. Our workplace consists of a diverse set of people from different nationalities, backgrounds and fields. As a postdoctoral researcher working with us, you receive the benefits of support in career
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strong confidence/belief in a vehicle’s perceived environment based on evidence (local & shared sensor readings) is under addressed, as such shared sensor readings may be subject to spatial correlations
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experimentation, isotopic measurements and modeling aspects taking advantage of a network of international collaboration and collaborations with the private sector. Importantly, this project is associated to a
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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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make a difference in the world! Position Information We are seeking a Postdoctoral Research Associate to assist the project leader (Dr. Srinivasulu Ale, Professor of Agrohydrology) in research projects