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domains such as telecom, defence and cloud. You will join the Machine-Intelligence for Networks and Distributed Systems (MINDS) research group at the Department of Computing and Learning Systems, School
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. Possible research topics include: Scientific machine learning Numerical partial differential equations (PDEs) Computational fluid dynamics Neural operators High-performance computing Data-driven
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Research Associate to develop, scale, and apply artificial intelligence (AI) and deep learning (DL) models for power grid systems. The successful candidate will contribute to scalable AI workflows for grid
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(adaptive) imaging strategies and reconstruction. The research combines ultrasound physics, signal processing, machine learning, computational imaging, and clinical translation. Beyond your individual
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building and applying state-of-the-art machine learning approaches, including foundation models, variational autoencoders (VAEs), and transformer-based architectures, to integrate single-cell and multi-omic
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developing new machine learning methodologies that tackle unique computational problems in healthcare applications. We use large real-world complex datasets, including data extracted from electronic health
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networks, RNNs, LLMs) or in the deployment of such algorithms. Experience with specialized computational architectures such as GPUs, FPGAs, neuromorphic processors, or machine learning accelerators
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transfer (CHT) or fluidic manifold optimization Experience with machine learning/AI (PyTorch or TensorFlow), reduced-order modeling (ROM), or data assimilation (DA) Experience with GPU programming (CUDA and
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Description The Clinical Artificial Intelligence Lab at NYU Abu Dhabi seeks to improve patient care by developing new machine learning methodologies that tackle unique computational problems in
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of AI for next-generation microbiome research? We are looking for a highly motivated researcher to develop novel machine learning and computer vision methods for autonomous live-cell microscopy