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operational employment. This doctoral research will thus leverage the power of graph neural networks – a novel ML architecture, capable of learning fundamental physical behaviour by modelling systems as graphs
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research areas: Generative AI for Medical Imaging and Digital Biopsies Develop and interpret deep neural networks (DNNs) for automating non-destructive tissue-based analyses using high-parameter medical
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additive manufacturing of lightweight structures to enable novel development of materials and process design. The PhD position will be supervised by Prof. Noomane Ben Khalifa (Hereon/Leuphana University
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chemical reaction networks with robotic systems and analytical science. You will also learn how to programme robotic systems and how to implement aspects of deep learning and neural networks for reservoir
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Appropriate computational skills and knowledge of programming languages (Python, C++, etc.) Experience with Machine and Deep Learning models and software (Keras, Scikit-Learn, Convolutional Neural Networks, etc
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identification of biological sounds using passive acoustic data. Passive acoustic monitoring will be conducted with species identification based on a neural network trained and tuned to the turbulent waters
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Applications are sought for a fully-funded 42 month PhD studentship to work with Dr Rachel Nicks and Prof Stephen Coombes on the project: White Matter Computation: Utilising axonal delays to sculpt
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, you will leverage the power of graph neural networks – a novel ML architecture, capable of learning fundamental physical behaviour by modeling systems as graphs and encoding nonlinearities in these. As
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/f/d, E13 TV-L, 50-75%) The position is limited for three years. Description of the project The research group of Prof. Dr. Frank Schreiber at the University of Tübingen deals with the physics
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. - Neural networks and machine learning strategies for the analysis of scattering data. Large amount of scattering data obtained in our group requires development of the advanced analysis techniques. In