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Field
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We invite you to apply for a 2-year postdoctoral position in the area of hybrid AI, combining modern language models (LMs) with ontologies and knowledge graphs (KGs). The position is part of a newly
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). This domain adaptation is crucial to ensure the generalization of the model to different types of sensors and environmental conditions. This aspect could notably be addressed by implementing graph-based
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, for their analysis and optimization, we use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our activities
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good results - Interest on topics around the area of distributed systems and data management - Basic knowledge in distributed systems and graph algorithms is desired - Hand-on experience with large-scale
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curriculum vitae of each nominee (if available, mandatory for self-nominations Please note that any further documents or appendices (like tables, graphs, etc.) will not be considered in the evaluation
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effects for drug discovery. The successful candidate will play a leading role in developing gene perturbation models that combine foundation models (FMs) and graph neural networks (GNNs) to accelerate
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Description Are you excited about using large-scale AI to accelerate scientific discovery? Join a Horizon Europe project developing next-generation scientific foundation models that combine knowledge graphs
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and different approaches can be tested to align the human and agent variants. The PD will experiment with symbolic techniques using Knowledge Graph representations of the world, Large Language Model
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to work in an interdisciplinary environment. Desirable Skills: Experience working with or supporting a scientific facility/instrument platform. Knowledge of graph-based methods, manifold learning
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learning methods. Develop deep learning architectures (e.g., variational autoencoders, graph neural networks, transformers) for cross-omics data representation and feature extraction. Apply multi-view