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workflows that integrate modern AI and machine learning concepts (e.g., surrogate models, adaptive sampling strategies) into the drug discovery pipeline to increase throughput and predictive accuracy
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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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theoretical evolutionary biology, including optimal control theory, life history modelling, adaptive dynamics, and population genetics. This position is part of the interdisciplinary consortium
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to generate baseline datasets for calibrating and validating predictive models of biodiversity-rich forests. Using machine learning (ML) algorithms, the Research Assistant will help predict the occurrence
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quantitative predictions testable against empirical data from diverse ecological contexts. We use methods from theoretical evolutionary biology, including optimal control theory, life history modelling, adaptive
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estimation of engineered tissue properties developing prediction models of engineered cartilage properties as a function of the mechanical loading regime. The main duty of the Doctoral Researcher (PhD Student