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population genetics/genomics. The focus of this postdoc will be on the application of Ancestral Recombination Graphs (ARGs) for spatial population genetic inference. Our work combines computational and
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of multi-agent coordination, decentralized control, target assignment, or swarm robotics. Familiarity with graph neural networks, attention mechanisms would be advantageous. Experience with computer vision
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(PINNs) and surrogate modelling Time-series modelling and anomaly detection Bayesian methods and uncertainty quantification Graph Neural Networks (GNNs) Spatiotemporal data engineering Digital twins and
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Postdoctoral Fellow with Professor Morgane Austern. Professor Austern’s group focuses on research in high-dimensional statistics, probability theory, machine learning theory, graph data, Stein method, ergodic
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anomaly detection Bayesian methods and uncertainty quantification Graph Neural Networks (GNNs) Spatiotemporal data engineering Digital twins and simulation Demonstrated Applied AI for Healthcare and
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. Research areas include Representation Learning, Machine learning and Optimization on graphs and manifolds, as well as applications of geometric methods in the Sciences. This is a one-year position with
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: Multimodal Information Retrieval: Developing novel retrieval frameworks that unify heterogeneous scientific data (text, tables, molecular graphs, images, time series) drawn from massive data lakes
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reliability. ; ; The research will explore graph-based representations of endoscopic examinations, anatomically structured learning, uncertainty estimation, and confidence-aware aggregation strategies, enabling
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research group. We are looking for excellent candidates with a background and experience in one or more of the following areas: graph algorithms, parameterized complexity, approximation algorithms, extremal
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system to track proposals. Evaluate and perform preliminary analysis of the data using graphs, charts or tables to highlight the key points of the research results collected in accordance with the research