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Postdoctoral Researcher in ML for Dynamical Systems Representation, Prediction, and State-estimation
to develop machine learning-enabled approaches for predictive modelling and state estimation for fundamental applications within physical sciences. Your role The main research responsibilities involve building
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models ignore this exposome. In BEE, we will build explainable, physics-guided, GeoAI-driven models that: Predict acute and chronic NCD risks at the population scale Identify vulnerable neighbourhoods and
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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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energy system models based on the institute`s own open-source FINE framework https://github.com/FZJ-IEK3-VSA/FINE. Your tasks in detail: Implementing geothermal plants with material co-production in
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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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, 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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. 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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, and how these molecules interact within cells to form complex functional networks. We are also working towards applications of our knowledge to address important real-world problems. PhD: 3-4 years full