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is searching for a Control Engineer for developing health-aware model predictive control (MPC) for fuel cell hybrid electric vehicles (FCHEVs). Fuel Cell HEVs provide a long-term solution to
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health. Please see our website for more information: gvnlab.bme.columbia.edu We expect the Staff Associate III to lead the development and application of advanced computational models to simulate, predict
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Post-Doctoral Associate in Sand Hazards and Opportunities for Resilience, Energy, and Sustainability
research on the integration of Digital Twins with AI/ML technologies for infrastructure lifecycle management. Develop and validate computational models for monitoring and predicting infrastructure
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effectiveness and toxicity of the treatments. Other duties: Develop and validate cancer risk prediction models using deep neural networks based on semistructured data. Develop and validate learning strategies
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fields). Strong quantitative skills and demonstrated expertise in predictive modeling and advanced computational methods (e.g., Multilevel Vector Autoregressive Models, Dynamic Structural Equation
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the flexibility and power of NNs with the ability of LMMs to robustly learn from structured and noisy (non i.i.d.) data, applying them on the prediction of both plants and human phenotypes. These models will
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quantitative predictions testable against empirical data from diverse ecological contexts. We use methods from theoretical evolutionary biology, including optimal control theory, life history modelling, adaptive
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to combine high-throughput metabolomics with 3D cell culture models Perform large chemical and genetic studies in cancer cell lines derived spheroids Develop predictive model of drug response by comparing 2D
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of whole plants at crop level. A central element is the plant’s 3D geometry, and models should predict plant growth, development, and yield as well as key physiological relationships across the whole plant
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selectivity and permeability and ultrahigh water permeability combined with high salt rejection. The objective of this work is to construct atomistic models of MOFs/Polymers and Artificial Water-Channel