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of mold free shelf-life predictive models, determining the number of variables as well that need to be recorded to be able to train the model; (ii) design and development of a model to predict mold growth
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thermomechanical processing and develop computational models to predict mechanical properties, their evolution, and formability. Through August 2026, contribute to the DOE ARDAP research project by evaluating
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of pharmaceutical formulation and manufacturing processes. The role The post holder will develop and implement mechanistic models to analyse and predict the behaviour of pharmaceutical processes. Your work will
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challenge is therefore to develop efficient surrogate models capable of rapidly predicting macroscopic mechanical properties directly from microstructural descriptors while preserving the underlying physical
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successful candidate will dedicate their efforts to the following specific research objectives: 1) Developing models for predicting the thermal runaway (TR), venting, and jet fire in a single cell with
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. - Conduct high-throughput serum proteomic analyses and integrate molecular datasets. - Validate candidate biomarkers in independent cohorts. WP3.2 – Integrated predictive modeling: - Develop integrative multi
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RAP opportunity at National Institute of Standards and Technology NIST Modeling Complex Microstructures Location Information Technology Laboratory, Applied and Computational Mathematics Division
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Dynamic Atomistic Predictions of Crystalline, Crystal Defect and Liquid Metal Properties NIST only participates in the February and August reviews. Classical interatomic potentials provide a means
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National Aeronautics and Space Administration (NASA) | New York City, New York | United States | about 7 hours ago
traditional predictive attempts and limits the availability of training data for high-resolution atmospheric and hydrological models. This limitation is compounded by the fact that many atmospheric reanalysis
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experimentally, followed by further model improvements, and implementation or design of a robust workflow and predictive design tool. Where to apply Website https://www.academictransfer.com/en/jobs/359149/engd