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, the postdoctoral researcher will be responsible for contributing to the development of advanced methodologies for predicting crystal structures (CSP) based solely on their chemical composition and atomistic modeling
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dataset generation technique to optimize the training of neural networks (NNs) for seismic data prediction. The use of neural networks to predict seismic velocity models has shown increasingly accurate and
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purely correlational analyses and to develop predictive models with operational relevance. Where to apply Website https://emploi.cnrs.fr/Candidat/Offre/UMR8212-DAVFAR-008/Candidater.aspx Requirements
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applications for a joint professorship under the Jülich Modell with the Hasso-Plattner-Institut für Digital Engineering gGmbH (HPI) to be filled as soon as possible: Full Professorship (W 3) for Software Testing
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Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description he project aims to develop a data-driven model to
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model introduced previously for carburizing will be further developed in this study. In this model, carbon diffusion is predicted using Fick's law and finite difference scheme. A source term accounts for
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to the weather prediction and climate projections. This is mainly due to our lack of understanding of cloud/snow ice microphysics and over-simplified representation in models. On a broader sense, although weather
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Center for Biologics Evaluation and Research (CBER) | Silver Spring, Maryland | United States | about 7 hours ago
—including translation efficiency prediction models, RNA secondary structure analysis, codon-pair and harmonization algorithms, and immunogenicity risk prediction tools—to help guide experimental design and
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to correct or account for these biases, and build predictive models that simulate biological responses to in silico perturbations such as genetic or pharmacological interventions. The project aims to advance
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interaction scores. Build and deploy machine learning and statistical models for functional genomics predictions, including sgRNA efficiency and drug sensitivity scoring. Collaborate with laboratory members