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Field
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. The key objectives include: Develop scalable Bayesian deep learning methods for uncertainty estimation in modern neural architectures. Design principled uncertainty modelling frameworks for multi
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. The key objectives include: Develop scalable Bayesian deep learning methods for uncertainty estimation in modern neural architectures. Design principled uncertainty modelling frameworks for multi
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validate data-driven methods that combine production data, predictive and state-estimation models, uncertainty-aware learning and constrained optimization to improve process consistency under process
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(e.g., low texture, illumination variation, dynamic scenes, or sensor degradation). The research will integrate model-based estimation methods (e.g., filtering, factor graph optimization, moving horizon
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communication skills, with motivation to publish in leading journals and conferences. Desirable Criteria Experience with advanced state estimation methods, including Extended/Unscented Kalman Filters and
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estimation methods, including Extended/Unscented Kalman Filters and particle filters. Knowledge of battery degradation, ageing, and state estimation (SOC, SOH, SOP), including diagnostics and prognostics
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an on-call. On-Call allowed to work 1039 hours in any twelve (12) month period. Unit Position Summary Professional Aide to support a research project focused on developing estimation and inference
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systematic comparison of key modelling approaches and loss functions for age estimation, as well as the development of new, robust methods. To this end: Approaches to be implemented and compared
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Munich, you will develop new methods in high-dimensional covariance estimation. Your job The position is part of an NWO-DFG funded research project on high-dimensional covariance estimation. You will
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spatiotemporal data, ● Methods for heterogeneous treatment effects estimation, ● Methods for multiple exposures, multiple outcomes, ● ML and AI methods for causal inference, ● Bayesian causal inference