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Field
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department. The project aims to advance predictive modeling frameworks for structural architected materials that exhibit complex instability and bifurcation phenomena. The research will involve close
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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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predictive machine-learning models from heterogeneous data. DSIP is actively collaborating with industrial partners and research organizations. DSIP is involved in developing Deep Learning solutions for time
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Vacancies Postdoctoral Researcher: High-Fidelity LES & Atmospheric Boundary Layer Modeling Key takeaways About the Project The ECOWIND project focuses on an integrated strategy to extend
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model biases, and identify sources of predictability. The project will involve; 1) rigorous interrogation of NOAA GFDL's CM4X simulation output with respect to coastal sea level variability and relevant
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such models, and working with a team of scientists interested in pushing the boundary of predictability. Position Requirements Recent or soon-to-be-completed PhD (completed within the last 0-5 years) in
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prediction of gene perturbation effects for drug discovery. The successful candidate will play a leading role in developing gene perturbation models that combine foundation models (FMs) and graph neural
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, and protein structure prediction by artificial intelligence algorithms. The goal is to generate functional models of multimeric protein complexes and how they assemble as a guide to understand disease
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spectroscopy, especially applied to the analysis of lipids or oils. Experience in the application of chemometrics to develop predictive models Participation in competitive research projects related to the field
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focus is on advancing the state-of-the-art in generative modeling and applying these advances toward developing more capable biotechnologies, including the design of more evolutionarily resilient