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Field
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LiDAR, IMU, camera, and wheel-odometry data in GPS-denied, low-light environments. Implement LiDAR-based or LiDAR-inertial SLAM, factor-graph or pose-graph optimization, loop-closure validation, drift
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such as transformers, self-supervised learning, multimodal learning, generative models, graph neural networks, or foundation models. Experience with structural and/or functional brain modeling. Familiarity
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Helmholtz Association of German Research Centres | Oldenburg Oldenburg, Niedersachsen | Germany | 3 months ago
documents, for example from the Prize Papers project; the creation of knowledge graphs using language models; and the automated analysis of knowledge graphs using methods from network science and cultural
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models, network analysis, graph-based learning, uncertainty quantification, scientific machine learning, or interpretable AI. This position is full time, on-site at the Penn State University Park campus
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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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systems, numerical analysis, stochastic problems and stochastic analysis, graph theory and applications, mathematical biology, financial mathematics and mathematical approaches to signal analysis
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. Relevant skills could include statistical analysis, data management and collection, causal inference, network analysis, graph theory, visualizations, and online tool development. Experience in conducting
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storage and analysis solutions (e.g., key-value stores, object or document storage, graph analytics systems) deployed on HPC computational and storage systems. Co-authorship of peer-reviewed publications
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diverse populations. The candidate will be funded primarily by an R01 on method development for statistical and population genetic applications based on the ancestral recombination graph (see recent
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, a proven publication record, and effective interpersonal skills. Preferred Qualifications: Knowledge of graph neural networks and other geometric deep learning approaches for graph-structured