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and research in several areas. These include, but are not limited to: Adversarial location and network interdiction models Adversarial machine learning attacks and defense (e.g., against Bayesian
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process of continuous germplasm improvement, discovery trait research and methodology optimization to reach greater breeding efficiency. The general research technologies/methodologies and approaches
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-Informed Neural Networks (PINNs) and hybrid models that respect the physical laws governing the real-world system Applying Deep Reinforcement Learning (DRL) algorithms to optimize processes within simulation
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structures, molecular networks, and disease-resistance phenotypes. Artificial intelligence (AI), machine learning, and bioinformatics will connect genotypes with phenotypes and identify maize and fungal genes
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data collected longitudinally across the post-infection study time course to optimize machine learning methods that predict disease outcomes and identify host factors and interactions that most heavily
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. Additionally, we intend to measure root water uptake using sap flow meters. The data will be integrated using recently developed physics-informed neural networks in order to translate apparent resistivity data
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Army Institute of Research (WRAIR) DoD's premier research enterprise for infectious diseases, brain health, and performance optimization, is offering this Postdoctoral research opportunity to conduct