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machine learning frameworks such as recurrent neural networks and transformers. Models and datasets will be studied and benchmarked in key tasks relating to both prediction/forecasting and anomaly detection
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neural networks and transformers. Models and datasets will be studied and benchmarked in key tasks relating to both prediction/forecasting and anomaly detection. Comparison with known analytic methods and
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effort at the intersection of machine learning and applied mechanics. The focus of this position is on extracting information about what a neural network has learnt in a symbolic and (human) interpretable
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. The high-level goal of the project is simple: to use anatomical knowledge and existing knowledge as training data for deep neural networks (instead of manual annotations). The research will be conducted
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directions will be pursued to enhance column generation using machine learning. The first line of research focuses on improving scalability by using Graph Neural Networks to identify and eliminate non
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theoretical research is focused on embodied neuroAI, recognising that the body influences biological neural networks, the continuity of actions, and sensory inputs. Leveraging advancements in Drosophila genetic
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generative modelling, and graph neural networks. Additional responsibilities include developing research objectives and proposals; presentations and publications; assisting with teaching; liaising and
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to understand, predict, and treat diseases. You will work with multimodal biomedical datasets including omics, imaging, and patient data and apply cutting-edge AI models such as graph neural networks, transformer
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multi-electrode arrays to evaluate the activity of neural network formation Testing the inter-laboratory reproducibility of the model between the BfR, Berlin, and the TiHo, Hannover Preparation
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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