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Field
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Overview Nature offers a mechanism - called homeostasis - by which life forms can maintain their physical integrity and well being. On the other hand, a series of machines, including robots, cannot
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strong need for accurate monitoring and forecasting of the Earth. Machine learning and signal processing play important roles here. The PhD project will focus on machine learning and signal processing
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tools such as LTSpice (preferred)/TINA/Multisim. Knowledge of low-power biopotential amplifier design and energy harvesting techniques is preferred. Candidates with machine learning skills, particularly
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learning (ML). Workers will engage in immersive, simulated construction tasks, while wearable sensors monitor their physical effort, emotional states, and cognitive load. Physiological and behavioural data
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are looking for a highly motivated and skilled PhD researcher to work on graph-based machine learning surrogates of wind energy systems. Our goal is to accelerate flexible fatigue load estimation
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parameter space of the electrolysis processes. DoE is required for data-efficient exploration and optimization within the process parameter space as well as for adaptive, data-driven machine learning to map
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Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung | Bremerhaven, Bremen | Germany | 3 months ago
the integration of ensemble machine learning models enhances the retrieval accuracy of phytoplankton biomass compared to traditional algorithms, and assess the implications for monitoring phytoplankton dynamics in
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/or reactor physics Documented knowledge/experience in machine learning What you will do As a PhD student, you will have the opportunity to shape your research project while receiving guidance and
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/technical challenges Project FITNESS will build upon and extend state-of-the-art methods [1], [2] recently developed within the team, showing to outperform existing, machine-learning based approaches in
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Aviation by 2050. This exciting doctoral project, in collaboration with Rolls-Royce, will develop innovative computer vision methods which when combined with optical flow velocimetry will enable imaging