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Field
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Assistant Professor Position in Transient Electromagnetic Signal Processing, Modelling and Inversion
machine learning, multidimensional inversion, and probabilistic geological modelling to enable efficient mapping in previously inaccessible terrains. The successful candidate will be employed primarily in
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applications for a Research Associate position in the area of efficient and interpretable machine learning systems. The successful candidate will work on projects involving ensemble learning, large-scale data
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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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will work closely with the Principal Investigator (PI), Co-PI, and the research team to develop deep learning-based computer vision algorithms and software for object detection, classification, and
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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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data integration and analysis Integrate phylogenomic and functional data using machine-learning approaches for candidate gene prioritisation Contribute to software and web-tool development Present
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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
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: The R2L Lab explores how language understanding improves machine learning efficiency and generalization. We are a leader in agentic benchmarks and evaluation; our platforms serve as primary evaluation