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Postdoctoral Researcher in ML for Dynamical Systems Representation, Prediction, and State-estimation
of uncertainty quantification techniques for the learnt models. You will also have opportunities to contribute to open-source computational tools and datasets, teach master-level courses, and advise doctoral
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or willingness to learn quickly. Publications, thesis work, or demonstrable projects in computer vision, multi-modal ML, digital twins or biomedical ML. Familiarity with uncertainty quantification and model
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, multidisciplinary team environment. Preferred Qualifications: Knowledge of uncertainty quantification methods and causal inference for complex environmental systems. Experience with large-scale Earth system
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Postdoctoral Researcher in ML for Dynamical Systems Representation, Prediction, and State-estimation
systems as well as towards designing observer-based state estimators from output timeseries data measurements. The research also involves development of uncertainty quantification techniques for the learnt
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directions within geometric learning and uncertainty quantification will also be available for you to explore. This PhD position offers a unique chance to influence the trajectory of our group's work and
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, or quantum-inspired methods Experience with hybrid quantum–classical algorithms or optimization methods Background in uncertainty quantification, reduced-order modeling, or machine learning Experience
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methods for LLMs, and (iii) develop a benchmark for uncertainty quantification in LLM-based scientific agents. The project will be carried out under the supervision of Martin Trapp and Hossein Azizpour, and
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networks, risk analysis or uncertainty quantification (preferred). Knowledge of data science in general as well as practical experience with conducting data science analyses with good programming skills
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of Lepton Number Violation to nuclear transition operators, computation of relevant many-body LNV operators in light nuclei for benchmarking and uncertainty quantification purposes, and the broader
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, ground-truth datasets, including methods for calibration, triangulation, and uncertainty quantification. Design and implement a robust translation layer that generalizes these 3D posture models to large