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Field
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, protocols, and data standards across collaborating institutions and scales. This collaboration will support the generation of coherent, high-quality datasets and enable the development of predictive models
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related engineering fields. The candidate should have skills and experiences in at least one of the following areas: 1) advanced data analytics for performance prediction and risk analysis of transportation
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National Aeronautics and Space Administration (NASA) | Fields Landing, California | United States | about 2 hours ago
is reproduced in the NASA Ames Electric Arc Shock Tube (EAST) facility, and is used to test models developed to predict the heating mechanisms. The primary models employed are the DPLR computational
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team to work on machine learning-supported rapeseed genomics and breeding. Your tasks: You design, train and interpret deep-learning models to predict regulatory gene variants in rapeseed genomes. You
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Medical Science and involves close collaboration with: The Halberg Group (University of Copenhagen) – experts in insect physiology and osmoregulatory control The Collin Lab (Lund University) – leaders in
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research. The Postdoctoral Scholar – Pharmacometrics in the PhASR will support data collection, quality control of data, data analysis, modeling, simulation and PK/PD study design for human clinical trials
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Rutgers Infrastructure Resilience Group. Both groups involve a number of research activities ranging from hydrologic hazard prediction to state-of-art infrastructure mapping, and are supported by various
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Develop and apply machine learning techniques and statistical analyses, including digital twin methodology, to fit and validate prediction model. Perform quality control and imputation of genotype and
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unique opportunity to work in an interdisciplinary team of applied mathematicians and ecologists at UCC and abroad, to participate in field trips to Lough Hyne, to develop predictive and data-driven
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accelerate translational research. Build or leverage novel artificial intelligence and machine learning (AI/ML) techniques for advanced liquid biopsy (ctDNA) and tissue transcriptomic (RNA-seq) data to predict