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Field
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on deep-learning methodologies to create fast emulators of large-scale geophysical models. The specific goal is to develop an emulator of the MIT general ocean circulation model (MITgcm) for a regional
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Details Title Postdoctoral Fellow in Deep Learning Theory and/or Theoretical Neuroscience School Harvard John A. Paulson School of Engineering and Applied Sciences Department/Area Position
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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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depends on the background of a suitable candidate. The main topics of the group in the past few years were generative modeling, 3D reconstruction, image-editing, and deep learning using 3D data. More
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polarisation shaping, and finally using deep learning to solve the complexity challenge associated with coherent beam combination. The role Within HiPPo, your specific task will be to develop a ‘digital fibre
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, such as, geometric/topological/algebraic data analysis, geometric/topological deep learning, Math for AI, categorical deep learning, sheaf neural networks, PINN/KAN models, neural operators, etc, and
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deep learning. You will contribute to several high-impact projects addressing hydrological extremes and their feedbacks within climate and human systems. Your work will have real-world impact, providing
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. This role provides an exciting opportunity to apply advanced machine learning and statistical modelling techniques to large-scale, high-dimensional eDNA datasets collected from Australian coastal and deep-sea
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The expected pay for this position is $70,000 per year + benefits. AI and Deep Learning for Genomics, Transcriptomics, and Bioinformatics Job Summary The School of Biomedical Engineering at the University
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populations and biobanks for risk prediction, genetic discovery, and genomic medicine. Federated and transfer learning for distributed and privacy-preserving data integration. AI and Deep learning approaches