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, systems engineering, geometry assurance, automation, or model-based development. The project addresses how advanced digital infrastructure can be used to reduce uncertainty, risk and variation in
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) study. This large-scale, longitudinal project is designed to advance computational psychiatry by integrating mechanistically informed behavioral tasks with theory-driven mathematical modeling in a
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machine learning, reduced-order modeling, or data-driven modeling of physical systems. You will conduct research on the development of fast, trustworthy, and data-efficient surrogate models that
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datasets and labeling processes. Machine Learning & Model Development Design, develop, optimize, and support machine learning and deep learning models. Prepare datasets for training, validation, and testing
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(NUS), and BEARS. The role will focus on developing and implementing AI/ML-driven high-throughput, closed-loop experimentation for the discovery and optimization of electrocatalytic materials for CO
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) environments, GPU-enabled systems, and cloud-based resources for large-scale data analysis and model development. Using these rich data resources, we develop and apply advanced AI/ML methods to model tumor
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, multiscale data analysis by integrating diverse datasets (e.g., in situ observations, remote sensing products, model simulations) to inform model development, calibration, and validation
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fitness for use in model development and validation. · Perform computational analysis of imaging, molecular, genomic, and phenotypic data to identify relationships among microenvironmental states
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the role. Core Responsibilities: Conduct research and development in federated learning, privacy-preserving machine learning, multimodal AI, and foundation model adaptation for biomedical and related
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intelligence of these systems are insufficient to cover the diversity of human-driven experimental activities. The development of multi-appendage, dexterous robots with embodied intelligence is a key to