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Field
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Implementing Bayesian networks and uncertainty quantification techniques to account for sensor noise and model confidence limits Designing, training, and fine-tuning computer vision models to extract clinically
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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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industrial contexts remains limited due to several structural limitations: · limited interpretability of model behavior; · weak guarantees regarding robustness and reliability
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European Molecular Biology Laboratory (EMBL) | Brandenburg an der Havel, Brandenburg | Germany | about 2 months ago
to ecosystems. EMBL has already established itself as a global leader in AI-driven innovation in biology research, with successes in areas including genomics, structural biology (e.g., AlphaFold), biomarker
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their dynamics to be formulated as systems of linear ordinary differential equations. Bayesian Optimization and Reinforcement Learning methods will be employed to solve the inverse problem of shape and flexibility
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carbon, nitrogen, and water flows in agroecosystems. A solid background in uncertainty quantification, applied statistics, Bayesian calibration, and Monte Carlo simulations. Strong skills in scientific
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Two DTU Tenure Track Assistant Professors in Autonomous Modelling and in Robotic Synthesis of Ene...
) closed-loop materials discovery, e.g., Bayesian Optimization, autonomous analysis of patterns, spectral data or cell-level testing. Experience with predictive control of the synthesis robotics and reaction
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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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the same systematics identified in the observables. d) Estimation of cosmological and “nuisance” parameters using Bayesian methods. 4. The research activities provided for the post-doc assignment will
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Learning models to understand and predict interactions in dynamic ecological networks. Our lab is looking for candidates for the following stipend: Learning the Structure and Dynamics of Complex Networks We