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Field
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skills: Experience developing artificial intelligence (AI) or machine learning (ML) models, particularly for time series or spatiotemporal data. Experience with representation learning, anomaly detection
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(specifically PCECs). Proven experience in developing and validating numerical models (e.g., using COMSOL). Hands-on experience with programming for numerical optimization, machine learning, and data processing
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) in Beltsville, MD conducts a combination of modeling and experimental research focusing on crop and soil response to abiotic factors and agroecosystems management. Research Project: During
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measurements and in the underlying physical models. Machine learning (ML) techniques can be exploited to identify common patterns in the data and augment the physical laws of wave propagation, leading in turn
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strong sense of community & inclusion Enjoy a career that makes a difference by collaborating & learning from the best At UNSW, we pride ourselves on being a workplace where the best people come to do
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generated synthetic representations. The project will also explore machine-learning approaches and efficient imaging strategies, including reconstruction of three-dimensional pore structures from radiography
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enrichment (GO, KEGG), network analysis, genome assembly and binning, systems biology, and multi-omics integration. Apply statistical modelling, machine learning, and deep learning approaches for biomarker
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on the developed models for agencies/commercial partners Supervise junior researchers and master students Job Requirements: Preferably PhD in Computer Engineering, Computer Science, Electronics Engineering or
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to groom the next generation of leaders, thinkers, and innovators to thrive in the digital age. Located in the heart of Asia, NTU’s College of Computing and Data Science is an ‘exciting place to learn and
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in numerical analysis, partial differential equations (PDEs), and scientific computing. Solid background in machine learning theories, with specific experience in Physics-Informed Machine Learning