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and machine learning, you will help develop new methods for understanding complex failure mechanisms—an area where existing industrial knowledge remains limited. The project will be executed in three
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streams—including data streaming to cloud databases, scientific visualization, and integration of machine learning workflows. Development of additional modules within commercial FE software (ABAQUS, MSC
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Communication, Singal Processing, Low Power Electronics, Wireless Sensing, Low-Power System Design, Machine Learning & Edge Inference, Underwater acoustic communication. Furthermore, you have a proven record of
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approach will create a unique foundation for advanced data analysis, including AI, machine learning, and statistical modeling, aimed at uncover the key traits that define successful microbial biofertilizers
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on the modelling and optimisation of PRO systems using advanced Computational Fluid Dynamics (CFD) and Machine Learning (ML) techniques. This role offers an exciting opportunity to contribute to cutting-edge
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proficient in ROS/ROS2, Python and/or C++/C# Knowledge and/or experience within one or more of the fields of acoustic sensing, hydrodynamics, and machine learning is a plus. You have strong communication
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Electrophysiological signal processing of, e.g., EEG, ECG, EMG, etc. Health data science, incl. modern machine, and deep learning methods, Cloud-based platforms like MS Azure or Google Colab Health data standards, like
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multidisciplinary research in energy markets, optimization, game theory, and machine learning. Our team of 13 members (link ), from 10 different nationalities, values diversity and includes experts from a range of