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techniques, including genetic algorithms, to optimize transducer architectures. 3, Develop computational mechanics workflows to predict material properties and guide the design of acoustic metamaterials
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Responsibilities : Spearhead the research and development of predictive models and strategic tools designed to support decision making in strengthening both domestic and international supply chains, particularly
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models for the fire resistance of LSF walls, including the development of machine learning models, experimental testing, and numerical simulations, within the project “FireLSF – Development of Predictive
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data integration, image segmentation, predictive modeling, and uncertainty-aware active learning approaches. Perform data analysis and collaborate on physics-based simulations to support closed-loop
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exciting opportunity to contribute to groundbreaking research developing AI-based models to predict cognitive decline in patients with cognitive impairment. The role emphasises computational and statistical
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complex magnets via interpretable machine-learning models, and develop improved AI models that can accelerate prediction of new synthesizable magnet candidates with high energy density and critical
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A postdoc position is available immediately in the laboratory of Dr. Daniel Blair which focuses on leveraging high-throughput chemical synthesis and MS/MS analysis to create predictive models
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an intense of microbial activity contributes into the process of stabilization of organic matter; Assess the effects of organo-mineral management on soil biological parameters, including soil fauna; Predict
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help develop new computational models that integrate molecular reaction networks with AI/ML algorithms in order to predict patient-specific cardiac remodeling and heart disease outcomes across human
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multispectral, hyperspectral, and RGB imagery. These models will integrate crop growth models, agronomic data, and genomic information to predict key wheat traits under varying climate conditions. The position is