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the undergraduate and graduate levels. Where to apply Website https://www.bth.se/english/vacancies/job/phd-student-position-in-software-engin… Requirements Research FieldComputer science » Computer systemsEducation
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their PhD. Project description The aim of this project is to deepen the fundamental understanding of machine learning through the lens of optimal transport theory, systems theory, and statistical physics
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plasticity platform. Different machine learning strategies will be explored to capture the complex relationships between microstructural features and mechanical responses. In particular, the project will
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on studying the principles of neural computation through recurrent neural networks, dynamical systems theory, and machine learning. - Develop mathematical and computational models of neural networks - Analyze
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& Collaboration The successful candidate will work at the interface of machine learning and biostatistics, developing new theory, algorithms, and scalable implementations. By establishing a new class of multi-frame
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is looking for an aspiring PhD candidate to research causal machine learning and uncertainty quantification for Earth Observation time-series. Currently, predictive AI in Earth Sciences relies heavily
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preparation and testing or powder mixtures, and then to devise predictive models (possible using machine learning approaches) for the estimation of mixture properties from pure component propeties. The PhD
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:10.1093/nar/gkaf1388), we will develop machine learning tools to model microbial communities and their impact. The environment: The successful applicant will work within the Hildebrand and Traka groups
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differential equation models of bacterial persistence. A particular challenge, both for simulation and for machine learning, lies in the high dimensionality of these equations, which causes grid-based numerical
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/deploying deep learning models and machine learning applications. Computer skills: Python (PyTorch, TensorFlow), databases (MySQL), 3D Slicer, ITK-SNAP, OpenCarp. Previous experience in research activity in