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Field
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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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. 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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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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: 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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zero-shot reasoning and scene-graph inference. Ensure the system is deployment-ready by supporting benchmarking of inference speed, compute efficiency, and scalability with concurrent agents. Enable real
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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
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on energy-efficient circuit design and software-hardware co-optimization, with exciting applications in graph-based prediction. What we’re looking for: A PhD in Electrical and Computer Engineering or a
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of behavioural models for dynamic analysis. Particular emphasis will be placed on the development of temporal modelling concepts, knowledge graphs, and formal verification techniques, enabling the automatic