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expertise in the field of immunology and bioinformatics and/or mathematical modelling Addition Qualifications: - Experience in applying machine learning, deep learning to immunology - Experience with
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Duties and Responsibilities Develop novel algorithms for ultrasound simulation, imaging, and quantitative analysis Customize machine learning / deep learning methods for image reconstruction Conduct
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deep learning, computer vision, or generative AI. Demonstrated experience with one or more deep learning frameworks (e.g., PyTorch, TensorFlow). Demonstrated ability to work with large-scale Earth
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graduate courses in biostatistics, statistics, artificial intelligence, machine learning, and data science. Participate in the mentoring of graduate students, postdoctoral fellows, and staff. Participate
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methods for image-based characterisation of complex structures. Experience with machine learning, image analysis, scientific computing, or data science is advantageous, but the project focuses on
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optical fibers using physics-informed deep learning experimental and simulation-based investigations of optical diffractive neural networks development and implementation of novel diffractive networks for
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computational text and image analysis; Machine learning, natural-language processing, large language models, or evaluation and auditing of AI and online platforms Causal inference, natural experiments
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learning, deep learning, brain-inspired learning, and optimization theory. The aim is to explore new technologies and methods for information processing. 2. Autonomous Intelligent Systems: Integrating
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, deep learning and virtual crash simulation to create more realistic and equitable safety assessment tools for tomorrow’s transport systems. About the research project This project aims to develop a
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. Image Processing & Annotation Preprocess ophthalmic imaging data for machine learning workflows through normalization, resizing, artifact reduction, and quality assessment. Coordinate imaging annotation