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structure prediction. Working at the intersection of generative AI and biophysics, the Fellow will focus on expanding the current framework to model dynamic protein ensembles. As an Empire AI-funded fellow
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for structural biology. This project sits at the intersection of X-ray scattering and deep learning, aimed at integrating experimental data to predict protein ensemble structures. As an Empire AI-funded fellow
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program Metavers, this project aims at conducting a theoretical study by numerical simulation of an ensemble of electrophilic aromatic substitution reactions activated by superacids, which will also be
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promising results in building prediction models, they are typically data-centric, lack context, and work best for specific feature types. Interpretability is the ability of an ML model to identify the causal
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that integrate structural predictions and neutron scattering data using ensembles instead of current single structure implementations. The integration of simulation and experiment will yield methods that can be
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of the Arctic Ocean, to assess its reliability (do the predicted error bars encompass the actual errors?), its inclusion into ensemble data assimilation, and its use in operational forecasting. The PhD fellow
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, scikit-learn, PyTorch, TensorFlow); additional experience with R, MATLAB, or Julia is an advantage. Machine Learning Expertise: Familiarity with causal machine learning, ensemble methods, and deep learning
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, scikit-learn, PyTorch, TensorFlow); additional experience with R, MATLAB, or Julia is an advantage. Machine Learning Expertise: Familiarity with causal machine learning, ensemble methods, and deep learning
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This post will advance the application of Machine Learning (ML) in weather forecasting and hydrological prediction. The Research Fellow will develop ML methods for postprocessing numerical ensemble weather
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of the Varda machine learning weather prediction system. The model is being trained using archive data from MeteoSwiss operational forecasts and observations, with the objective to provide accurate and fast