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- Wydział Matematyki Fizyki i Informatyki UG
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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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to work effectively in an interdisciplinary team. PREFERRED QUALIFICATIONS Experience with one or more of the following: knowledge graphs, graph machine learning, link prediction, representation learning
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Applied to Neuroscience and Drug Discovery Where to apply Website https://gestiononline.bioef.eus/ConvocatoriasPropiasBiobizkaia/es/Convocatorias… Requirements Research FieldOtherEducation LevelPhD
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, graphs); experience with analysis and processing of large volumes of data; development of reproducible scientific software; proficiency in Python and libraries (Pandas/NumPy and PyTorch/TensorFlow/Scikit
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use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our activities are experimentally driven and
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application! We are looking for a postdoctoral researcher to work on the fundamentals of knowledge graphs and virtual data integration. Work assignments You will actively participate and lead work tasks in two
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perturbation models that combine foundation models (FMs) and graph neural networks (GNNs) to accelerate therapeutic target identification. GenePPS aims to overcome current limitations of perturbation modelling
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is to develop computationally efficient reduced-order dynamical systems on graph with modern power grid systems as an application. Education and Experience: Applicants must have recently completed a
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use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our activities are experimentally driven and
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research contributions will include designing algorithms for concept and structure extraction, building neural/graph hybrid models for pedagogical reasoning, implementing ontology-alignment methods for cross