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Field
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, Applied Mathematics, or a related field. Strong expertise in: Computer vision and image processing Deep learning (e.g., PyTorch, TensorFlow) Data-driven modeling and analysis Proficiency in Python and
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constitutive modeling of anisotropic and compressible materials. Minimum Qualifications Required PhD in mechanical engineering, aerospace engineering, materials science and engineering, civil engineering
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complementary expertise in scientific machine learning and constitutive modeling of anisotropic and compressible materials. Minimum Qualifications Required PhD in mechanical engineering, aerospace
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, numerical method, and data-driven/machine-learning approaches, including: · Numerical simulation of kinetic equations: fast algorithms with stability/convergence analysis; · Data-driven and machine
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-informed or data-driven (AI/ML) manufacturing approaches Demonstrated ability to work effectively in collaborative research environments Does this position have supervisory responsibilities? No Preferred
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-informed or data-driven (AI/ML) manufacturing approaches Demonstrated ability to work effectively in collaborative research environments Does this position have supervisory responsibilities? No Preferred
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modeling, multimodal data fusion, interpretable machine learning, NLP, text mining, or automated extraction of materials data from the literature Experience with workflow automation, data infrastructure
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modeling. · Supervise and mentor research assistants in performing experiments, analyzing data, and interpreting results. · Conform to all laboratory cell culture, biosafety, data
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-generation, data-driven manufacturing systems that integrate artificial intelligence, real-time sensing, and digital twins to transform how critical components are designed, produced, and qualified. The
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complex, multi-omics datasets to uncover novel metabolic bottlenecks and identify subsequent targets for cell line engineering beyond model-driven predictions. Protein-metabolite interaction modeling