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Field
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combine density functional theory (DFT), molecular simulations, and machine-learning force field (ML-FF) development to uncover the factors controlling NHC–surface interactions and to model realistic
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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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partner from data sciences provides data management and AI based Image analysis, an internal simulations group working on quantitative models to reproduce and predict experimental data, and an internal
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, and clinical data. - Apply machine learning and foundational modeling to support predictive or exploratory analyses. - Collaborate with interdisciplinary teams to refine multi-modal pipelines
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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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National Aeronautics and Space Administration (NASA) | Fields Landing, California | United States | 2 months ago
control, and coronagraph system modeling. Location: Ames Research Center Moffet Field, California Field of Science:Planetary Science Advisors: Natasha Batalha natasha.e.batalha@nasa.gov 650-604-2813 Ruslan
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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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variants as functional and assess their impact on gene expression. Contribute to large-scale modeling of engineered traits to predict performance and optimize design. Required Qualifications: PhD in
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attention to scientific rigor and interpretability Experience with XAI tools (SHAP, LIME, Integrated Gradients) to identify which features of the model are driving the predictions Clear written and verbal
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of switchable RNA nanostructures. Develop databases for RNA modules for automated building of atomistic models. Develop multistate sequence design algorithm for rational design of RNA switches. Develop database