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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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on agentic approaches, where an LLM interacts with visual tools, which may themselves be neural networks. Central challenges include enabling LLMs to reason about visual structures, designing
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are developed, modelled and controlled. You will create novel adaptative, physics-informed models that tightly integrate thermo-fluid dynamic laws, deep learning neural networks, and experimental data. A key
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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
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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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convolutional/neural networks Experience with explainable and interpretable AI (XAI) Rules governing PhD students are set out in the Higher Education Ordinance chapter 5, §§ 1-7 and in Uppsala University’s rules
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communications. Evaluation of model performance can be conducted based on the data collected through the water tank. We have the GPU machines ($14k) to develop deep neural networks for underwater communications