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Field
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methodological innovation to real-world clinical application. You will work with multimodal biomedical datasets (omics, imaging, spatial data, and patient data) and apply cutting-edge AI technologies such as graph
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techniques including graph neural networks, Bayesian neural networks, conformal prediction intervals and generative AI for synthetic data generation. You will also develop frameworks for uncertainty
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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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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
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for load forecasting in scenarios where current models fall short, such as extreme weather events, grid incidents and high variability in renewable energy. You will explore techniques including graph neural
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Associate Research Scientist / Post-Doctoral Associate in the Division of Science (Computer Science)
include: Strong foundation in one of the following areas: Machine Learning / Information Retrieval / Knowledge Graph Representation / Recommender Systems Graph Theory/Network Science Python, and up-to-date
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at least two of the following areas: AI/machine learning for biological modeling (e.g., virtual cell, foundation models, graph neural networks, or multimodal omics integration). Epigenetics (DNA methylation
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prediction of gene perturbation 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
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to the large-scale nature, complexity, and heterogeneity of 6G networks, for their analysis and optimization, we use tools such as artificial intelligence/machine learning, graph theory and graph-signal
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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