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be used to combine these datasets while accounting for their different spatial scales, uncertainties and sampling frequencies. Machine-learning methods may also be explored for retrieval, bias
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PhD student (m/f/d) in the field of mathematics, scientific computing, physics or an engineering discipline with a proven strong focus on numerical methods Berlin Division 8.4: Acoustic and
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and theoretical work. You will learn how to collect and analyse data within your research area as well as communicate your results at national and international conferences and in scientific journals
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developments in machine learning, the project investigates how runners’ shared social identity can influence their movement synchrony, physical load, and interaction with the running infrastructures. Using
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the Department of Materials Science and Engineering within the Faculty of Engineering. Project title: Processing intelligence for green metals using in situ X-ray characterisation and machine learning. We
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practices, and technology-enhanced learning. It is an advantage if you have one or more of the below A solid foundation in understanding learning processes from a cognitive, embodied, and/or epistemic
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. The research will involve training machine-learning models on large structure and sequence datasets and integrating membrane-specific biophysical constraints to enable the design of membrane proteins and
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of mill and production operations. The scientific challenge will be to use the model and machine learning alongside live mill data (temperature, rolling loads etc) to reverse engineer the current
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to conduct research activities in the field of Brain-Computer Interfaces, in the scope of the project “BCI4ALL”, co-funded by national funds through the Portuguese Foundation for Science and Technology, I.P
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nutrition, such as: analysis of time series data and dynamic processes, where signals and responses evolve over time. statistical modelling, AI, and machine learning on large epidemiological cohorts, diet and