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developing machine learning surrogates and emulators for dynamical systems. Proficiency in managing large datasets and training with GPU-enabled computing resources. Expertise in numerical optimization and
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, and network optimization for 6G networks and FutureG wireless networks. Successful candidates will have the chance to work with top-notch researchers from both academia and industry on future wireless
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simulations and multiscale spatial-omics data. • Integrate uncertainty quantification into scientific machine learning workflows and optimize the design of computational (ABM) and wet-lab experiments
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system optimization for remote construction. This position will focus on advancing research in construction assembly science and technology, logistics optimization, and real-time communication frameworks
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machine learning—for chemical and biological applications. You will design and implement models ranging from molecular to process scales, develop model-predictive control and optimization strategies, run
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: Initiate and maintain cell cultures Prepare and maintain biological stocks Maintain proper lab and experimental records, both digital and hard copy Test, optimize, troubleshoot new and existing methods and
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uncertainty quantification into scientific machine learning workflows and optimize the design of computational (ABM) and wet-lab experiments. • Collaborate with mathematical modelers and experimentalists in
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machine learning—for chemical and biological applications. You will design and implement models ranging from molecular to process scales, develop model-predictive control and optimization strategies, run
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-of-the-art sparse algorithm in matrices, tensor and networks for large-scale numerical, scientific and AI models and disseminating findings through publications and presentations in top-tier peer-reviewed
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to numerous preclinical research projects focused on the development of novel molecular magnetic resonance imaging (MRI)-based techniques for early detection, disease phenotyping and monitoring treatment