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Postdoctoral Researcher in ML for Dynamical Systems Representation, Prediction, and State-estimation
for predictive modelling and state estimation for fundamental applications within physical sciences. Your role The main research responsibilities involve building cutting edge machine learning techniques
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Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association | Dresden, Sachsen | Germany | about 2 months ago
control theory (e.g., model predictive control, fuzzy control, etc.) # Excellent teamwork and communication skills in an interdisciplinary and international research environment # Motivation and self
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Description The University of Wuppertal (Germany) invites applications for a PhD position (Research Assistant) in the group of Prof. Peter Zaspel, starting March 1, 2026. The position is part of
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-making processes to autonomous control and analytics. This PhD project aims to investigate and design novel abstractions, models, and algorithms that enable the superimposition of human-in-the-loop
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. The objective of this PhD project is to develop AI methodologies for the analysis part of condition monitoring (CM) and predictive maintenance (PM). The primary challenge in predictive maintenance lies in
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, you will join an agile team composed of: • A PhD student in AI/Control: focused on anomaly detection in time series. • An MLOps Engineer: responsible for deployment and production of models
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school for 260 PhDs, including industry PhDs and postdocs. Fellows are recruited to the 11 participating host universities/organizations, but brought together under the DDLS program, which has four
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apply AI and data-driven modelling to predict system efficiency - balancing air purification with energy consumption. It will also explore how sensor feedback can control treatment systems and communicate
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- specific predictive models, the lack of explainability in AI-driven decision processes, and the difficulty of capturing long-term dependencies in time-series data. In this project, you will focus
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remains poorly understood. The objective of this PhD is to characterize and model the biomechanical consequences of menopausal tissue remodeling in five pelvic-related soft tissues: skin, fascia, muscle