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on “Maternal Immune Activation” involving the development of novel artificial intelligence methods (graph and geometric deep learning, LLMs, …) working on methods for predictive multi-omics integration
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and experience: Essential criteria PhD in bioinformatics, computational biology, or a related discipline * Extensive experience and expertise in analysing/ training models on biological or chemical
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Lab (MaTRIX Lab) develops advanced computational and AI methodologies to decode complex biological systems and accelerate discoveries into translational impact. The lab integrates deep learning, graph
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to develop a knowledge-aware and event-centric framework for natural language understanding, in which event graphs are built as reading progresses; event representations are learned with the incorporation
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the theory of quantum graph states. Additional expertise in computational methods would be useful but is not necessary. The Postdoctoral and Senior Research Associate positions will also involve
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represented by a graph, which is a collection of nodes that are connected to each other by edges. The nodes represent the objects of the network and the edges represent relationships between objects. A common
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learning, small data learning · Active learning, Bayesian deep learning, uncertainty quantification · Graph neural networks This position involves active participation in a well-funded
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of the Postdoc Research Fellows are the following: Research: Work on novel AI/Data Science research with crucial interdisciplinary scope using machine/deep learning, generative/agentic AI, and knowledge graphs
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of sparse matrix, tensor and graph algorithms on distributed and heterogenouscomputational environments. Basic Qualifications: A PhD in Computer Science, Applied Mathematics, Computational Science, or related
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publications and presentations. Collect, analyze and graph data, conclude research projects in a timely manner, write reports, and manuscripts. Engage in career development activities, apply for dedicated