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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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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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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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-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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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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, 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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- 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
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and validation of a predictive pipeline for excipient–biologic interactions Integration of experimental SAXS data with AI-driven structural modeling to predict oligomerization behavior and excipient